Good Papers: read the good ones

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MEND: RL For Flow Models via Proximal Velocity Matching

MEND uses proximal velocity matching to cap rewards and accept only cost-effective sample moves, outperforming prior flow-model RL methods in far fewer updates without KL penalties or reference models.

Shreshth Saini, Neil Birkbeck, Yilin Wang, Balu Adsumilli and 1 more

Published Oct 5, 2026 · ▲ 2 on Hugging Face · Code ★ 1

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83%Must read

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arXivDeep RL

Homomorphic Advantage Operator: Stabilizing Reinforcement Learning Under Fully Homomorphic Encryption Constraints

The Homomorphic Advantage Operator stabilizes FHE-based reinforcement learning by centering TD targets to eliminate Bellman drift, achieving zero approximation-bound breaches and 18-point accuracy gains without extra multiplicative depth.

Abid Mohamed Nadhir, Ahmad Al Hanbali, Beggas Mounir

Published Oct 1, 2026 · 0 citations

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76%Highly rated

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arXivDeep RL

iADD: Improving Alignment and Diversity in Diffusion Policy Optimization

iADD analyzes diffusion policy optimization to show only-latter-timestep updates harm diversity, then proposes incremental Feynman-Kac training that improves alignment-diversity tradeoffs across tasks.

Ashok Prasad Neupane, Saugat Adhikari, Pramish Paudel, Ajad Chhatkuli and 1 more

Published Oct 1, 2026 · 0 citations · ▲ 4 on Hugging Face · Code

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arXivHuawei Technologies Imperial College London University College London Deep RL

Tail-Influence Sampling for CVaR Policy Evaluation

Tail-Influence Sampling allocates evaluation budgets by tail influence to estimate CVaR with oracle variance and lower MSE than rollouts.

Pauline Bourigault, Xiaotong Ji, Matthieu Zimmer, Rasul Tutunov and 1 more

Published Sep 29, 2026 · 0 citations · ▲ 23 on Hugging Face · Code ★ 1

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74%Highly rated

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NeurIPS 2026DeepMind Deep RL

Delightful Distributed Policy Gradient

Delightful Policy Gradient gates distributed updates with delight (advantage times surprisal) to suppress high-surprisal failures while preserving rare successes, outperforming importance-weighted methods under staleness, bugs, and corruption.

Ian Osband

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

0% Readers0 of 1 upvoted

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NeurIPS 2026Can Tho University and VinUniver Can-Tho VinUniversity U Aizu Deep RL

Modeling quantum neural network gradient with reinforcement learning

RLQ-Grad uses reinforcement learning to propose quantum neural network updates without differentiating circuits, avoiding barren plateaus and scaling with parameters rather than Hilbert space dimension to achieve orders-of-magnitude faster training and higher accuracy.

Nhan Luu, Trung D Luu, Ngoc Nam Pham, Thang C Truong

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

100% Readers1 of 1 upvoted

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NeurIPS 2026Xi'an Jiaotong Shanghai University of Finance a The Chinese University of Hong K SGIT AI A*STAR Deep RL

A Primal-dual Approach for Semi-Infinitely Constrained Reinforcement Learning

Di Wang, Liangyu Zhang, Haishan Ye, Guang Dai and 1 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Xidian Deep RL

Reinforcement Learning for View-Adaptive Distillation in 3D Gaussian Compression

Hongji Zhao, Mingrui Zhu, Xin Wei, Nannan Wang

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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67%Highly rated

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NeurIPS 2026U California, San Diego UC San Diego Deep RL

Safe Score Matching: Diffusion Policies with Hamilton-Jacobi Reachability for Online Safe Reinforcement Learning

Boyang Li, Matthew Kim, Sylvia Herbert

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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45%Niche pick

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NeurIPS 2026The Ohio State Deep RL

Finite-Memory Control of POMDPs: Fundamental Limits and Efficient Design

Emrecan Kutay, Atilla Eryilmaz, Ness Shroff

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026U Wisconsin-Madison U Wisconsin - Madison U Wisconsin -- Madison Deep RL

Distributionally Robust Multi-Task Reinforcement Learning via Adaptive Task Sampling

Nicholas Corrado, Wenyuan Huang, Josiah Hanna

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Shanghai Jiaotong Central South Sensetime The Chinese University of Hong K SenseTime Deep RL

ROLLVERIFY: BRIDGING EFFICIENCY AND ACCURACY IN LONG-TAIL ROLLOUT REINFORCEMENT LEARNING

Yongqiang Yao, Jingru Tan, Kaihuan Liang, Zixin Yin and 4 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Pusan National Deep RL

Accelerating Safe Reinforcement Learning with Massive Parallelism

Joonyoung Lim, Younghwan Yoo

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U Wisconsin–Madison U Wisconsin - Madison Deep RL

When Does Knowing the State Help? Diagnosing Process vs. Outcome Reward Design

Wenpei Shao, Ross Jacobucci

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026U Science and Technology of Chin The Hong Kong University of Scie Mohamed bin Zayed University of Hong Kong Polytechnic HKBU / RIKEN Deep RL

Interactive Combinatorial Reinforcement Learning for Knowledge Graph Reasoning

Jun Nie, Yonggang Zhang, Tongliang Liu, Chengqi Zhang and 2 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Dalhousie Deep RL

Instability of Meta-Learning Intrinsic Rewards for Policy Gradient Reinforcement Learning

Dilith Jayakody, Domenic Rosati, Janarthanan Rajendran

Paris Poster Session 5, Fri, Dec 11, 11:30 AM–1:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026Pusan National Deep RL

LOCU: Löwdin-Orthogonalized Constraint Updates for Multi-Constraint Policy Optimization

Joonyoung Lim, Younghwan Yoo

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Renmin University of China SUN YAT-SEN UNIVERSITY U the Chinese Academy of Science Tsinghua Deep RL

Exploring Lifelong Adaptation: In-Context Reinforcement Learning in Non-Stationary Environments

Ye Wang, Kaiqian Cui, Xinrun Xu, Tao Zhang and 1 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026TU Darmstadt Deep RL

Cross-Question Reliable Reinforcement Learning

Hector G. Rodriguez, Marcus Rohrbach

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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67%Highly rated

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NeurIPS 2026ML & Alignment Theory Schola Google DeepMind Deep RL

Designing Effective Monitor-Based Interventions for Mitigating Reward Hacking During RL

Aria Wong, Joshua Engels, Neel Nanda

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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45%Niche pick

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NeurIPS 2026d_model ML & Alignment Theory Schola Stanford MATS Research Google DeepMind Deep RL

Gradient Routing Localizes and Removes Unintended Behaviors in RL

Jake Ward, Shawn Hu, Aria Wong, Nathan Hu and 3 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Indian Institute of Technology, Purdue Deep RL

A Refined Sample-Complexity Analysis of Robust Policy Optimization under Decaying Actor Stepsizes

Swetha Ganesh, Vaneet Aggarwal

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026Carnegie Mellon CMU, Carnegie Mellon School of Computer Science, Carn Deep RL

Implicit Goal Conditioning via Value Disaggregation

Shashwat Saxena, Mehul Goel, Sreyas Venkataraman, Sarvesh Patil and 1 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U California, Santa Barbara U California, Santa Cruz U Chicago Virtue AI , University of California, Sant Deep RL

NitroBox: Lightning-Fast Sandbox for Large-Scale RL Training

Yuzhou Nie, Ruilin Zhou, Zhaorun Chen, Jingyang Zhang and 5 more

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026U Tsukuba, Tsukuba U Tsukuba LY Corp.U Tsukuba / RIKEN AIP Deep RL

Reward-Estimated Hypergradient for Bilevel Reinforcement Learning with Black-Box Follower

Shigeki Kusaka, Mikoto Kudo, Takumi Tanabe, Akifumi Wachi and 1 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U Massachusetts Amherst Department of Computer Science, Deep RL

Uncertainty-Guided Reward Labeling for Reinforcement Learning under Limited Feedback

Renhao Zhang, Shreyas Chaudhari, Bruno Silva

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026Tsinghua national university of singaore,U Science and Technology Beijing Deep RL

Bootstrapped Bipartite Actor-Critic for Diffusion RL

Tianze Zhu, yinuo Wang, Letian Tao, Tianyi Zhang and 5 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Qiyuanlab Beijing Institute of Technology Qiyuan Lab Academy of Military Sciences Deep RL

EDEN: Emergent Dynamics in Evolutionary Neural-networks for Robust Continuous Control

Chi Zhang, Jinge Li, Yifei Wang, Lei Wang and 1 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026HKUST Hong Kong University of Science Department of Computer Science a The Hong Kong University of Scie The Hong Kong University of Scie Deep RL

CPA: Efficient and Stable FP4 RL Training via Cross-Precision Alignment

Gu Gong, Yining Wei, Yuechen Tao, Tianyuan Wu and 10 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026China University of Petroleum China University of Petroleum china university of petroleum Huazhong University of Science a Deep RL

Mitigating Compounding Errors in Online Reinforcement Learning via Optimal Transport Regularized Flow Matching

Boxiang Tao, Lei Guo, Bin Wang, Zexin Wang and 1 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Weill Cornell Medicine, Cornell Cornell Deep RL

On-Policy Hindsight Distillation for Early Risk Prediction

Qiannan Zhang, Fei Wang

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026U Illinois Urbana-Champaign UIUC Google Research U Illinois at Urbana-Champaign U Illinois, Urbana Champaign Deep RL

RL-Inf: Tracking Non-local Training Data Influence for Online Reinforcement Learning

Shixuan Liu, Cheng Tang, Yuzheng Hu, Fan Wu and 2 more

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026Hanoi University of Science and Deep RL

BECON: Belief-Conditioned Constrained Multi-Objective Reinforcement Learning under Drifting Preferences and Budgets

Bui Trong Duc, Huynh Thi Thanh Binh

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Australian National Deep RL

Generalised Linear Models in Deep Bayesian RL with Learnable Basis Functions

Jingyang You, Hanna Kurniawati

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Tianjin Montreal Institute for Learning Shanxi Deep RL

What Kind of Diffusion Models Do We Need in Online Reinforcement Learning?

Zihao Wu, Hongyao Tang, Yi Ma, YAN ZHENG and 2 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Beijing Jiaotong U Electronic Science and Technol Institute for AI Industry Resear Horizon Robotics Carizon Deep RL

Higher-Order Action Supervision Makes A Strong Policy Class

Peng Cheng, Yunxian Hou, Zhi Zhou, Qian Zhang and 2 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Massachusetts Institute of Techn Harvard Deep RL

Hindsight Relabeling is All You Need for Reach-Avoid Learning

Kevin Li, Marinka Zitnik

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Korea Advanced Institute of Scie KAIST Deep RL

Towards a Unified Model for Flexible Job Shop Scheduling Problems

Inguk Choi, Woo-Jin Shin, Sang-Hyun Cho, Hyun-Jung Kim

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Mila - Quebec Artificial Intelli Universitat Pompeu Fabra Mila, Université de Montréal Mila - Quebec Artificial Intelli U Montreal Deep RL

A Surrogate Perspective on Convergence of Fixed-Target DQN

Zichu Liu, Nneka M Okolo, Ryan D&#x27;Orazio, Danilo Vucetic and 2 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Tulane Deep RL

Behavior-Discriminative Reward Shaping for Reward-Robust Reinforcement Learning

Zixuan Liu, Fangzheng Wu, Brian Summa, Zizhan Zheng

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U Washington Deep RL

Ordinal Geometry Complements Reconstruction: Diagnosing Planning with Compressed Value Functions

Shiheng Zhang

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026DGIST Daegu Gyeongbuk Institute of Sci Korea Advanced Institute of Scie Deep RL

H-GenPO: Hierarchical Generative Policy Optimization via the Option-Critic Framework

Wonhyeok Choi, Minwoo Choi, Jaeyeul Kim, Kyumin Hwang and 2 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Shenzhen Shenzhen Technology University, Shenzhen Technology Tencent AI Lab Deep RL

Foundation Pareto Flow Policy for Multi-Objective Reinforcement Learning

Zhanjiang Yang, Lijun Sun, Yueming Li, Meng Li and 1 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026École Polytechnique Huawei Technologies Ltd.Shenzhen Research Institute of B Deep RL

Adaptive Scheduling Pipeline For Multi-Instance Asynchronous Reinforcement Learning

Salah Chikhi, Luis H Ruiz, Entong Li, Li Zeng

Paris Poster Session 1, Wed, Dec 9, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026Xiamen U York Deep RL

DGRAF: Observation-Quality-Aware Reinforcement Learning for Dynamic Reconfigurable Batteries

Jiasong Chen, Jingwei Hu, Zheng Fang, Zhihong Zhang

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026The Hong Kong University of Scie Nanjing Tencent AI Lab U Science and Technology of Chin Tongji Deep RL

Canopy: Tree-Aware Rollout Scheduling for Agent Reinforcement Learning

Feiyuan Zhang, LI Pengbo, Ziniu Li, Yuhao Jiang and 8 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Tsinghua Tsinghua University, Tsinghua Nankai Beijing Union Alibaba Group Deep RL

Feasible Policy Optimization for Safe Reinforcement Learning

Yujie Yang, Yuanxu Sun, Wenyu Li, Beiyan Jiang and 2 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026The Hong Kong Polytechnic McGill University & Mila - Q Hong Kong Polytechnic Tongji university Tongji Deep RL

Preference-Guided Adversarial Policy Optimization for Long-Tail Robust Driving

Tong Nie, Yihong Tang, Junlin He, Yuewen Mei and 4 more

Paris Poster Session 4, Thu, Dec 10, 5:30 PM–7:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026Southeast University Sh Southeast U Nottingham Ningbo China Imperial College London Deep RL

Reconciling Operational Energy Trilemma: A Heterogeneous Risk-Constrained MDP Framework with Residual Policy Learning

Yujian Ye, Siqi Qian, Yizhi Wu, Tianxiang Cui and 1 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Alberta Machine Intelligence Ins U Alberta Deep RL

Learning Reusable Options by Decomposing Neural Policies

Parnian Behdin, Reza Abdollahzadeh, Kiarash Aghakasiri, Levi Lelis

Paris Poster Session 5, Fri, Dec 11, 11:30 AM–1:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026Northwestern Polytechnical The Chinese University of Hong K Zhejiang Xi'an University of Posts a Deep RL

S$^2$-RL: Sample-Set Dual Reinforcement Learning for Generative Semantic Segmentation Dataset Distillation

Haoyu Wang, Fei Zhou, Qingqing Qiu, Lei Zhang and 2 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Florida Atlantic Deep RL

Grouped Adaptive Head Mixing for Personalized Multi-Task Federated Reinforcement Learning

Yiran Pang, Zhen Ni, Dimitris Pados, Xiangnan Zhong

Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026U Oxford U Warwick Deep RL

Subprocess-Constrained Markov Decision Processes

Jiarui Gan, Debmalya Mandal

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Boston Boston University / Broad Instit Deep RL

Learning to Undo: Transfer Reinforcement Learning under State Space Transformations

Mridul Mahajan, Aldo Pacchiano, Xuezhou Zhang

Paris Poster Session 2, Wed, Dec 9, 5:00 PM–7:00 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026Institute of automation, Chinese Institute of Automation, Chinese CASIA Deep RL

Distribution-Adaptive Policy Optimization

Yuxiao He, Ziqi Wang, Xingzhou Lou, Xiaoqian Liu and 1 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Texas A&M University - Colle CSAIL, MIT Google Deepmind / Harvard Harvard Deep RL

Iterative Gumbel Planning for Continuous Control

Shaohuai Liu, Weirui Ye, Yilun Du, Le Xie

Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026Beijing Jiaotong Beijing jiaotong univercity Deep RL

Decoupling is the Key: Scaling Deep Value Networks in Reinforcement Leanring

Yunsheng Xue, Ziyi Zhang, zhihao wu, Youfang Lin

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Deep RL

Underlying Functional Structure of Reinforcement Learning

Ezgi Korkmaz

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Hong Kong Polytechnic The Hong Kong Polytechnic The University of Tokyo UNSW Sydney Deep RL

Discovering Programmatic Policies from Reinforcement Learning-Based Traffic Signal Controllers

Lindong Xie, Yang Zhang, Beiyu Song, XING Zeren and 2 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Google Research Google Research & Courant In Research Scientist, Google Resea Deep RL

SAMPPO: Structure-Aware Mirror Proximal Policy Optimization

Corinna Cortes, Mehryar Mohri, Yutao Zhong

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026Beijing Institute of Mathematica Capital Normal Deep RL

Physics-Informed Optimal Control by Control-Only Supervision with Error Guarantees on Value and Policy

Zihua Wang, Xiaopei Jiao, Yunfeng Cai

Paris Poster Session 3, Thu, Dec 10, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026Bocconi Politecnico di Milano Deep RL

Constrained MDPs with Trajectory Constraints

Martino Bernasconi, Matteo Castiglioni, Alberto Marchesi

Paris Poster Session 4, Thu, Dec 10, 5:30 PM–7:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026SpotlightSeoul National Deep RL

Distributionally Robust Domain Randomization with Learned Risk-Sensitive Dynamics Samplers

Sukchul Jeong, Insoon Yang

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Universitat Pompeu Fabra Deep RL

Solving Stochastic Control under Multiplicative and Internal Noise via Constrained Optimization

Ruben Moreno Bote, Francesco Damiani, Dmytro Grytskyy

Paris Poster Session 4, Thu, Dec 10, 5:30 PM–7:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026UC Berkeley, University of Calif U California, Berkeley UC Berkeley Deep RL

Learning Process Rewards via Visitation Matching for Efficient RL

Raymond Tsao, Andrew Wagenmaker, Sergey Levine

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026Duke Deep RL

Ordered Policy Optimization

Dongkyu (Derek) Cho, Pan Xu

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U Luxemburg Beijing Institute of Technology U the Sunshine Coast Deep RL

Prevailing Bisimulation Metric Learning Is Biased: Implicit Regularization and Its Remedy

Junqi Lu, Ruixiang Sun, Xin Li, Gaopeng Peng and 2 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U Wisconsin - Madison Department of Computer Science, UW-Madison Deep RL

Anatomy of Off-Policy Policy Gradient: Importance Sampling, KL Regularization, and Baselines

Haoqun Cao, Yurun Yuan, Tengyang Xie

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Huazhong University of Science a The Hong Kong University of Scie U Hong Kong Hong Kong University of Science Deep RL

The Cost of Mismatch: Noise Amplification in Zeroth-Order Reinforcement Learning

Lianmin Chen, Junbin Qiu, Chenxing Wei, Yao SHU and 1 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Indiana Deep RL

A New Framework for Quantum Reinforcement Learning

Alireza Tavakoli, Mohsen Heidari

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026beihang university Peking Institute of Software Chinese Ac Tsinghua University, SEEN-E Robo Beijing University of Aeronautic Deep RL

Adaptive Robust Estimator for Policy Optimization in Reinforcement Learning

Zhongyi Li, Wan Tian, Jingyu Chen, Kangyao Huang and 7 more

Paris Poster Session 2, Wed, Dec 9, 5:00 PM–7:00 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026Hunan Central South Deep RL

$R^2E$: A Role-driven Reward Evolutionary Framework for Automated Reward Function Design

Shouhao Chang, Xuan Liu, Hongye Zhu, Xinning Chen and 1 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Pennsylvania State MIT Deep RL

A Constrained Bi-level Optimization Framework for Constrained Preference-Based Reinforcement Learning

Yue Mao, Siyuan Xu, Shicheng Liu, Minghui Zhu

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026The Hong Kong University of Scie Hong Kong University of Science Deep RL

DiversePlace: Diversity-Seeking Curriculum Reinforcement Learning for Macro Placement

Wenrui Zhou, Jiashun Liu, Wenji Fang, Zhiyao Xie and 1 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026OpenAI U Oxford Independent Deep RL

Mitigating Reward Hacking via Task Representations

Lillian Sun, Joe Benton, Eric Easley

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Aalborg U Roma "La Sapienza"Deep RL

RL-Guided Contraction of Symbolic Tensor Networks for Quantum Circuit Equivalence

Suhaib Al-Rousan, Christian Schilling, Max Tschaikowski, Kim Larsen

Paris Poster Session 5, Fri, Dec 11, 11:30 AM–1:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026Georgia Institute of Technology Northumbria Deep RL

EpiPivot: Learning to Control the Simplex Method under Epistemic Uncertainty

Guantao Zhao, Mahdi Noorizadegan, Shihao Yang, Nicoleta Serban

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026U Bristol Cardiff U Macau Bristol Robotics Laboratory, Uni Deep RL

Learning to Search, Searching to Learn: A Closed-Loop Framework for Large-Scale Vehicle Routing

Yongji Fu, Yi Zhou, Gaojie Jin, Guanqun Cao

Paris Poster Session 3, Thu, Dec 10, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026Sungkyunkwan Sung Kyun Kwan Deep RL

Control First, Robustness Next: Decoupled Representation Learning for Visual RL Generalization

heo chanyong, Hyelyn Jeong, Jongchan Park, Seungjun Oh and 1 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026ExperienceFlow and Mila McGill University / Mila / DeepM Deep RL

Permanent and Transient Representations for Continual Reinforcement Learning

Nishanth Anand, Doina Precup

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Seoul National Deep RL

Risk-Sensitive Deep Optimal Stopping

Yechan Jeong, Youngjoon Hong

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Sungkyunkwan Sung Kyun Kwan Sungkyunkwan university Deep RL

Skill-Adaptive Noise Scheduling for Diffusion Policies

Woo Kyung Kim, Gwangpyo Yoo, Eunyoung Park, Honguk Woo

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Harvard Harvard Medical School + Kempner Deep RL

Effectiveness of Curriculum Learning Depends on Reward Sparsity and Competing Optima

John Vastola, Ann Huang, Satpreet Harcharan Singh, Samuel J Gershman and 1 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Nanyang Technological Sun Yat-Sen Tencent Tsinghua University, Tsinghua U Chinese Academy of Sciences Deep RL

DUDS: Dual-stage Data Selection for Efficient Reinforcement Learning with Verifiable Rewards

Hongling Zheng, Li Shen, Zichuan Lin, Jiafei Lyu and 5 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U Wisconsin - Madison UW-Madison U Wisconsin-Madison Deep RL

Entropy Dynamics of Agent Reinforcement Learning

Wendi Li, Shawn Im, Sharon Li

Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026Emory Harvard U Wisconsin - Madison Stony Brook Deep RL

Provably Efficient Representation Learning for Low-Rank CMDPs

Kaixuan Liu, GUOJUN XIONG, Shengpu Tang, Wanyun Si and 1 more

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026Shanghai Jiao Tong Shanghai Jiaotong The Chinese University of Hong K shanghai jiaotong university Deep RL

DSSP: Diffusion State Space Policy with Hierarchical Full-History Conditioning

Zhiyuan Guan, Jianshu Hu, Han Fang, Yunpeng Jiang and 4 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026The University of Hong Kong U Hong Kong Deep RL

Multi-Stage Planning from Single-Stage Data: Reinforcement Learning Helps Composition but Requires Anchoring

Boyuan Zheng, Zidong Liu, Yingyu Liang

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026U Edinburgh The University of Edinburgh Deep RL

Local Policy Manifolds for Efficient Multi-Objective Reinforcement Learning

Qiyue Xia, Tianwei Wang, J. Michael Herrmann

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026TU Delft, QuTech Delft University of Technology Deep RL

Replay-buffer engineering for noise-robust quantum circuit optimization

Akash Kundu, Sebastian Feld

Paris Poster Session 1, Wed, Dec 9, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026HPE Labs Hewlett Packard Enterprise Infor Vanderbilt Deep RL

C2G-BENCH: A Cyber-Physical Evaluation Benchmark for Hierarchical Reinforcement Learning in Grid-Interactive Hyperscale Data Centers

Vineet Gundecha, Sahand Ghorbanpour, Sifat Abdullah, Mahasweta Chakraborti and 5 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Wharton Statistics, Amazo U North Carolina at Chapel Hill Deep RL

From POMDP Theory to Deep RL with Particle Filters

Kevin Tan, Ziping Xu

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026Northwestern northwestern Deep RL

Variance-Optimal State Resampling for Reinforcement Learning with Verifiable Rewards

Yibo Wang, Yuanxin Liu, Zhaoran Wang

Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026U Toronto U Massachusetts Boston IIT Madras San Jose State U Alberta Deep RL

Reinforcement Learning Agents Are Swimmers

Juan Rojas, Jacob Adamczyk, Abhishek Naik, Volodymyr Makarenko and 5 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026The Ohio State University, Colum Deep RL

Sharp Convergence and Sample Complexity of Policy Mirror Descent for Average-Reward MDPs

Enes Arda, Atilla Eryilmaz

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026Google Intrinsic Technische Universität Darmstadt TU Darmstadt Deep RL

JAXtari: High-Throughput and Easy-to-Modify Arcade Learning Environment

Quentin Delfosse, Raban Emunds, Paul Seitz, Sebastian Wette and 4 more

Paris Poster Session 1, Wed, Dec 9, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026Eindhoven University of Technolo Nanyang Technological Delft University of Technology Eindhoven University of Technolo Deep RL

PDHFormer: Progressive Dual-Head Transformer for Behavioral Choice Prediction

Hao Zhou, Jing Chen, Yaoxin Wu, Jie Gao and 1 more

Paris Poster Session 4, Thu, Dec 10, 5:30 PM–7:30 PM, Paris Poster Hall · Published 2026

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67%Highly rated

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NeurIPS 2026Singapore Management Hong Kong University of Science Singapore Management University Deep RL

Diffusion-Enhanced GFlowNet for Solving Vehicle Routing Problems

Ni Zhang, Zhiqin Zhang, Ling Pan, Hoong Chuin Lau and 1 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Georgia Institute of Technology College of Computing, Georgia In Johns Hopkins Deep RL

Decision-Focused Learning in MDPs: An Occupancy Measure Approach

Zihao Zhao, Ashwath K Karunakaram, Ali Eshragh, Yuexing Li and 1 more

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026Singapore Management Nanyang Technological Nanjing University of Informatio National University of Singapore Deep RL

SimpleEvol: Efficient Intelligence Conversion via Less Human Prior in Automated Heuristic Design

Jianghan Zhu, Cong Zhang, Rongjie Zhu, CHI ZHANG and 1 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026SpotlightETHZ - ETH Zurich U Warsaw ETH Zurich Ideas NCBR, Polish Academy of Sc UC Berkeley & Amazon Deep RL

When Does Non-Uniform Replay Matter in Reinforcement Learning?

Michal Korniak, Mikołaj Czarnecki, Yarden As, Piotr Miłoś and 2 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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57%Worth a look

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NeurIPS 2026U Massachusetts Boston State University of New York at SPY Lab -- ETH Zurich Beijing Institute of Technology KAUST Deep RL

Differentially Private Sparse Reward Estimation with Preference Feedback

Meng Ding, Mingxi Lei, Jie Zhang, Jinyan Liu and 1 more

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026UT Austin The University of Texas at Austi U Texas at Austin UT Austin / NVIDIA Deep RL

Meta-Reinforcement Learning with Zero-Shot Reinforcement Learning

Jake Grigsby, Siddhant Agarwal, Yu Lei, Leonidas Varveropoulos and 1 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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78%Highly rated

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NeurIPS 2026U Alberta Deep RL

Deep Double Q-learning

Deep Double Q-learning trains two independent Q-functions to decouple selection and evaluation, reducing overestimation and outperforming Double DQN across 47 of 57 Atari games.

Prabhat Nagarajan, Martha White, Marlos C. Machado

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Vrije Universiteit Amsterdam VU University Amsterdam VU Amsterdam Deep RL

Hadamard Representation: Scaffolding Performance Across Model-free RL

Hadamard Representation replaces hidden layers with element-wise products of two layers, reducing neuron dormancy and increasing effective rank to consistently improve model-free RL performance.

Jacob Eeuwe Kooi, Zhao Yang, Mark Hoogendoorn, Vincent Francois-Lavet

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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71%Highly rated

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NeurIPS 2026RIKEN AIP U Edinburgh Deep RL

PPO in the Fisher-Rao geometry

FR-PPO leverages Fisher-Rao geometry to provide monotonic policy improvement guarantees and sub-linear convergence without dependence on state or action space dimensions.

Razvan-Andrei Lascu, David Siska, Lukasz Szpruch

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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71%Highly rated

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NeurIPS 2026U California, Los Angeles UCLA Deep RL

TriSearch: Learning to Optimize Triangulations via Bistellar Flips

TriSearch uses reinforcement learning and circuit-based flip representations to optimize triangulations across dimensions, discovering more Calabi-Yau triangulations than existing samplers.

Yiran Wang, Guido Montufar

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026Tsinghua Li Auto Inc.JD.com Horizon Robotics Tsinghua University, Tsinghua Deep RL

CRAFT: Counterfactual-to-Interactive Reinforcement Fine-Tuning for Driving Policies

CRAFT combines dense counterfactual advantages with grounded residual corrections to reduce variance and bias in closed-loop autonomous driving fine-tuning, achieving strong Bench2Drive gains.

Keyu Chen, Nanfei Ye, Yida Wang, Wenchao Sun and 3 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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74%Highly rated

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NeurIPS 2026Seoul National Sungshin Women's Deep RL

Reward Inflation: A Healthy Stimulus for Reinforcement Learning

Gradually scaling rewards during training accelerates RL adaptation by upweighting recent transitions, sustaining gradients to prevent neuron dormancy, and improving performance across diverse tasks.

Ganghun Lee, Minji Kim, Minsu Lee, Byoung-Tak Zhang

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026College of William and Mary William & Mary Deep RL

HyCO: A Hybrid Neural Solver for Combinatorial Optimization

HyCO hybridizes RL and diffusion solvers for combinatorial optimization, theoretically and empirically achieving lower regret via adaptive prefix construction and regime-switching.

Yuheng Li, Di Yang, Haipeng Chen, Yanhai Xiong

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026The Pennsylvania State Pennsylvania State Deep RL

Preference Conditioned Multi-Objective Reinforcement Learning: Decomposed, Diversity-Driven Policy Optimization

D³PO fixes multi-objective RL via decomposed per-objective updates, late preference weighting, and diversity regularization to recover dense Pareto fronts with one policy.

Tanmay Ambadkar, Sourav Panda, Shreyash Kale, Jonathan Dodge and 1 more

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026U Washington Deep RL

Factorized Spectral Representations for Reinforcement Learning

FaStR applies CP tensor decomposition to transition dynamics via contrastive learning, yielding separate state, action, and next-state encoders that shrink sample complexity and enable cross-actuator transfer.

Junyi Wu, Dan Li

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026Mila / Université de Montréal Mila - Quebec Artificial Intelli McGill U Montreal, Mila Mila, U. Montreal Deep RL

Representation Learning Enables Scalable Multitask Deep Reinforcement Learning

Predictive representation learning combined with high-capacity value approximation drives scalable multitask RL, with the simple model-free MR.Q outperforming world-model methods across continuous control tasks.

Johan Obando Ceron, Lu Li, Scott Fujimoto, Pierre-Luc Bacon and 2 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Nanyang Technological Mila - Quebec Artificial Intelli U Oxford U Montreal, Mila Deep RL

Beyond One-Size-Fits-All: Diagnosis-Driven Online Reinforcement Learning with Offline Priors

Diagnosis-driven tension management adapts online RL to deployment-specific prior validity shifts, rejecting universal benchmarks for flexible, evidence-guided optimization.

Guozheng Ma, Lu Li, Zilin Wang, Pierre-Luc Bacon and 1 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026UC Davis Microsoft U Southern California U California, Santa Cruz U California, Davis Deep RL

Video Models Can Reason with Verifiable Rewards

VideoRLVR applies reinforcement learning with verifiable rewards to video diffusion models, improving rule-consistent visual reasoning and cutting training latency 40% via early-step optimization.

Tinghui Zhu, Sheng Zhang, James Yipeng Huang, Selena Song and 4 more

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026 · ▲ 9 on Hugging Face · Code ★ 28

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NeurIPS 2026Apple Massachusetts Institute of Techn Deep RL

TGPO: Temporal Grounded Policy Optimization for Signal Temporal Logic Tasks

TGPO decomposes signal temporal logic into timed subgoals and invariant constraints for hierarchical reinforcement learning, achieving 31.6% higher success rates than baselines on complex long-horizon robotics tasks.

Yue Meng, Fei Chen, Chuchu Fan

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026 · ▲ 3 on Hugging Face · Code ★ 2

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72%Highly rated

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NeurIPS 2026Seoul National Georgia Institute of Technology Deep RL

FLAG: Flow Policy MaxEnt-RL by Latent Augmented Guidance

FLAG augments states with flow latents to localize sampling and prevent importance weight collapse, enabling scalable high-dimensional expressive MaxEnt-RL policies with state-of-the-art performance.

Sungha Kim, Gawon Lee, Jusuk Lee, Jonghae Park and 2 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026The Chinese University of Hong K Northeastern Southeast The Chinese University of Hong K The Chinese University of Hong K Deep RL

ReFPO: Reflow Regularization for Flow Matching Policy Gradients

ReFPO adds explicit reflow regularization to flow matching policy gradients, stabilizing training and enabling high-fidelity one-step inference that matches multi-step performance across control tasks.

Ge Wang, Yibo Peng, Fan Feng, Shenhao Yan and 9 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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78%Highly rated

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NeurIPS 2026Stanford Stanford Deep RL

Long-Horizon Q-Learning: Accurate Value Learning via n-Step Inequalities

Long-horizon Q-learning penalizes n-step value bound violations via hinge losses to stabilize bootstrapping and outperform standard TD methods.

Armaan A Abraham, Lucy Xiaoyang Shi, Chelsea Finn

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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76%Highly rated

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NeurIPS 2026Department of Computer Science Nanyang Technological Deep RL

Equivariant Reinforcement Learning for Clifford Quantum Circuit Synthesis

A qubit-relabeling-equivariant, size-agnostic reinforcement learning agent synthesizes near-optimal Clifford circuits across qubit counts, outperforming Qiskit on large instances.

Richie Yeung, Aleks Kissinger, Rob Cornish

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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71%Highly rated

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NeurIPS 2026Cortical Labs Honda Research Institute Tether Evo, Tether Operations Royal Melbourne Institute of Tec Cortical Labs Pty Ltd Deep RL

Embodied Neurocomputation: A Framework for Interfacing Biological Neural Cultures with Scaled Task-Driven Validation

The Embodied Neurocomputation framework optimizes biological neural network encoding for closed-loop navigation, identifying 12 configurations that outperform silicon DQN agents under identical interaction budgets.

Johnson Zhou, Daniel Tanneberg, Forough Habibollahi, Alon Loeffler and 11 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Korea Advanced Institute of Scie Korea Advanced Institute of Scie Deep RL

Bellman Residual Minimization for Control: Geometry, Stationarity, and Convergence

Bellman residual minimization for policy optimization achieves stable convergence with function approximation but lacks extensive study; foundational control results are established.

Donghwan Lee, Hyukjun Yang

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026New York Carnegie Mellon Syracuse Columbia Google Deep RL

The Smart Buildings Control Suite: A Diverse Open Source Benchmark to Evaluate and Scale HVAC Control Policies for Sustainability

The Smart Buildings Control Suite is an open-source HVAC benchmark using multi-year data from 11 buildings and scalable simulators to test control policies across diverse climates and structures.

Judah Goldfeder, Victoria Dean, Zixin Jiang, Xuezheng Wang and 3 more

Paris Poster Session 2, Wed, Dec 9, 5:00 PM–7:00 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026Stanford Stanford Deep RL

FASTER: Value-Guided Sampling for Fast RL

FASTER traces test-time sampling gains to earlier denoising stages via a denoising-space MDP that filters action candidates early, reducing compute while improving diffusion-based RL policy performance.

Perry Dong, Alexander Swerdlow, Dorsa Sadigh, Chelsea Finn

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026George Mason NICT Deep RL

Verifying Neural Networks with Reinforcement Learning

RSB uses reinforcement learning to optimize neural network verification branching heuristics, solving 11% more instances and cutting branch exploration by 50%.

Hai Duong, Thanh Le, ThanhVu Nguyen

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026Ohio State University, Columbus Deep RL

Learning to Cut: Reinforcement Learning for Benders Decomposition

RLBD trains a reinforcement learning policy to adaptively select Benders cuts, substantially improving computational efficiency and generalizing across problem variations.

Haochen Cai, Xian Yu

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026TU Munich U Twente HEC Paris Deep RL

Breaking the Grid: Distance-Guided Reinforcement Learning in Large Discrete Action Spaces

DGRL enables efficient reinforcement learning in discrete action spaces up to 10^20 via distance-guided exploration and regression-based updates, improving performance by up to 66%.

Heiko Hoppe, Fabian Akkerman, Wouter van Heeswijk, Maximilian Schiffer

Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026Instinct Robotics, Carnegie Mell Carnegie Mellon Deep RL

Absolute State-wise Constrained Policy Optimization: High-Probability State-wise Constraints Satisfaction

ASCPO guarantees high-probability state-wise safety constraints in model-free RL without strong assumptions, significantly outperforming existing methods on continuous robot control tasks.

Weiye Zhao, Feihan Li

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026U Virginia Deep RL

On the Divergence of Differential Temporal Difference Learning without Local Clocks

Differential temporal difference learning converges with local clocks but can diverge with global clocks in average-reward reinforcement learning.

David Antrobius, Shangtong Zhang

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026U Virginia Deep RL

Latent Q-Barrier Shielding for Safe In-Context Reinforcement Learning

Latent Q-Barrier shielding filters actions via predicted future costs to improve safe in-context reinforcement learning reward-safety tradeoffs under out-of-distribution deployment shifts.

Minjae Kwon, Amir Moeini, Shangtong Zhang, Lu Feng

Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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71%Highly rated

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NeurIPS 2026Albert-Ludwigs-Universität Freib CS Department, University of Fre U Freiburg Deep RL

Spectral Alignment in Forward–Backward Representations via Temporal Abstraction

Temporal abstraction acts as a low-pass filter reducing effective successor representation rank to fix spectral mismatch in forward-backward representations and stabilize long-horizon continuous control learning.

Seyed Mahdi Basiri Azad, Jasper Hoffmann, Iman Nematollahi, Hao Zhu and 2 more

Paris Poster Session 1, Wed, Dec 9, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026OralU Göttingen Princeton Max Planck Institute for Biologi Deep RL

Unifying Goal-Conditioned RL and Unsupervised Skill Learning via Control-Maximization

Unifying GCRL and MISL as control maximization reveals formulation-specific bounds linking diverse pretraining skills to downstream goal sensitivity.

Alireza Modirshanechi, Benjamin Eysenbach, Peter Dayan, Eric Schulz

Paris Poster Session 2, Wed, Dec 9, 5:00 PM–7:00 PM, Paris Poster Hall · Published 2026

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12/20 AI panelreviewers recommend it

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NeurIPS 2026U California, Irvine The University of Texas at Dalla George Washington U California, Santa Barbara Deep RL

TOPPO: Rethinking PPO for Multi-Task Reinforcement Learning with Critic Balancing

TOPPO balances critic gradients to fix PPO's multi-task ill-conditioning, outperforming SAC baselines with fewer parameters and steps.

Yuanpeng Li, Rui Miao, Gefei Lin, Annie Qu

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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15/20 AI panelreviewers recommend it

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NeurIPS 2026U Warsaw U Warsaw, NoMagic.AI UC Berkeley & Amazon Deep RL

Reward-Conditioned Reinforcement Learning

RCRL conditions agents on reward parameterizations via replay counterfactual rewards, improving sample efficiency and enabling zero-shot behavioral adaptation without extra interaction.

Michal Nauman, Marek Cygan, Pieter Abbeel

Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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9/20 AI panelreviewers recommend it

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NeurIPS 2026York Imperial College London Deep RL

Decoupling Time and Risk: Risk-Sensitive Reinforcement Learning with General Discounting

Flexible discounting in distributional reinforcement learning captures expressive temporal and risk preferences, fixing existing multi-horizon optimality issues.

Mehrdad Moghimi, Anthony Coache, Hyejin Ku

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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7/20 AI panelreviewers recommend it

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76%Highly rated

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NeurIPS 2026Purdue Deep RL

Natural Policy Gradient as Doubly Smoothed Policy Iteration: A Bellman-Operator Framework

Natural policy gradient equals doubly smoothed policy iteration, a Bellman-operator framework achieving global geometric convergence and O((1-γ)^{-1} log ε^{-1}) iteration complexity without modified stepsizes or regularization.

Phalguni Nanda, Zaiwei Chen

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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10/20 AI panelreviewers recommend it

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NeurIPS 2026Huazhong University of Science a Kuaishou- 快手科技 Wuhan Tsinghua Deep RL

TMPO: Trajectory Matching Policy Optimization for Diverse and Efficient Diffusion Alignment

TMPO replaces scalar reward maximization with trajectory-level reward distribution matching via Softmax Trajectory Balance, improving diffusion alignment diversity by 9.1% while avoiding reward hacking and mode collapse.

Jiaming Li, Chenyu Zhu, Zhiyuan Ma, Nanxi Yi and 8 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026 · ▲ 3 on Hugging Face

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16/20 AI panelreviewers recommend it

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NeurIPS 2026U Chicago U Michigan - Ann Arbor Fermilab U Michigan UChicago Deep RL

Learning to Trigger: Reinforcement Learning at the Large Hadron Collider

Reinforcement learning agents adapt Large Hadron Collider trigger thresholds online to maximize signal efficiency while maintaining background rates within tolerance bands, improving in-tolerance intervals by up to 56% on real CMS collision data without fine-tuning.

Zixin Ding, Shaghayegh Emami, Giovanna Salvi, Cecilia Tosciri and 6 more

Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026 · ▲ 6 on Hugging Face · Code ★ 3

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NeurIPS 2026Sorbonne Université ISIR, UMR 7222 Sorbonne Deep RL

Behavioral Foundation Models for Quality Diversity

BFM-QD searches a behavioral foundation model's latent space for diverse high-performing policies and outperforms parameter-space quality-diversity methods, especially in sparse and deceptive tasks.

Nazim Bendib, Nicolas Perrin-Gilbert, Olivier Sigaud

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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14/20 AI panelreviewers recommend it

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71%Highly rated

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NeurIPS 2026National University of Singapore Nanyang Technological Central South national university of singaore,Deep RL

Learning to Solve Compositional Geometry Routing Problems

DiCon solves compositional geometry routing via differential attention suppressing weak actions and contrastive learning for robust representations, yielding broad generalization.

Mingfeng Fan, Jianan Zhou, Jiaqi Cheng, Yifeng Zhang and 2 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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7/20 AI panelreviewers recommend it

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