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NeurIPS 2026NortheasternCarnegie MellonExploration

Markovian Experimental Design under Concept Drift

Ayberk Yarkin Yildiz, Lili Su, Carlee Joe-Wong, Edmund Yeh and 1 more

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

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NeurIPS 2026TongjiShanghai Jiao TongNVIDIAExploration

Value-Priced Uncertainty: A Family of PPO-Compatible Exploration Bonuses

qili shen, Xuanhong Chen, Ang He, Dake Zhang 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 2026Renmin University of ChinaCoupangTencent AI LabExploration

Self-Evolution Reasoner: Continuous Optimization via Policy-Intrinsic Exploration and Exploitation

Wenhang Shi, Yiren Chen, Zhe Zhao, Jinhao Dong 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 2026MILA & McGillMcGillMila - Quebec AI Institute, UnivMila / École Polytechnique de MoMcGill University / Mila / DeepMExploration

Addressing Sparse-Rewards in RL with Scalable Hierarchical Novel Eigen Options

Priyesh Vijayan, Élodie Côté-Gauthier, Mathieu Reymond, Sarath Chandar and 2 more

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

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NeurIPS 2026East China NormalChinese Academy of SciencesEast China Normal UniveristyExploration

OPSRL-SSP: Optimistic Posterior Sampling for Stochastic Shortest Path with Minimax-Optimal Regret

YIMING XU, Xian Wei, Cheng Chen

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

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NeurIPS 2026NorthwesternUniversity College London, UniveDukeExploration

The Type Theory of Stationary MDPs: Rare Events and Uncertainty Quantification

Imon Banerjee, Sayak Chakrabarty, Ramkrishna Jyoti Samanta, Riddhiman Bhattacharya

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

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NeurIPS 2026National Tsing HuaCyberAgent / TohokuExploration

Adaptive Joint Testing of Policies in Discounted Markov Decision Processes

Po-An Wang, Kaito Ariu

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

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NeurIPS 2026PekingInstitute of automation, ChineseTencent Corporate Development GrAlibaba GroupAlibaba Inc.Exploration

Iterative Scarcity-Guided Exploration: Bootstrapping Generative Auto-bidding from Narrow Support

Qingmao Yao, Guangzheng Hu, Yusen Huo, Chu Xu 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 2026Criteo AI LabExploration

Improved Regret bounds in Tabular Reinforcement Learning under Local Differential Privacy

Hugo Richard

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

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NeurIPS 2026Oxford, University of OxfordU OxfordExploration

Task-Driven Bayesian Experimental Design Yields Singly Intractable Objectives for Joint Policy Training

Tom Rossa, Angus Phillips, Thomas Rainforth

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

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NeurIPS 2026DataCanvasU SouthamptonExploration

ZetaEvolve: Learning to Search through History-Conditioned Potential Value

Jiyang Shen, Xiaojing Zhang, Bochen Lyu, Zhanxing Zhu

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 Science, BanExploration

Exploration as Constrained Policy-Space Optimization

Victor Azad

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

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NeurIPS 2026Chung-AngColumbiaColumbiaSeoul NationalExploration

Variance-Adaptive Optimal Algorithm for Reinforcement Learning with MNL Function Approximation

Wonyoung Kim, Garud Iyengar, Assaf Zeevi, Min-hwan Oh

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

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NeurIPS 2026Chinese Institute for Brain ReseChinese Institute for Brain ReseExploration

Human-Inspired, Task-Dimension-Guided Exploration for Efficient Learning and Transfer in High Dimensions

Fanyu Zhu, Jiahui An, Ni Ji

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

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NeurIPS 2026Institute of Automation, ChineseInstitute of automation, ChineseCenter for Excellence in Brain SExploration

Learning Contextual Causal Dynamics for Robust Exploration in Reinforcement Learning

Jiaming Pu, Yibo Zhang, Xu Dong, Tielin Zhang 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 2026KAISTKorea Advanced Institute of ScieU WashingtonExploration

Mitigating Overgeneralization in RND via Spectral Target Design

Minseok Jeong, Yechan Lee, Hyewon Choi, Jeongyong Yang 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 2026Istituto Italiano di TecnologiaItalian Institute of TechnologyIIT & UCLUniversity College LondonExploration

Reward-free Pretraining for Reinforcement Learning via Occupancy Coverage Maximization

ROVER pretrains exploration policies via occupancy coverage maximization using a resolvent world model and virtual sink state, yielding stronger initializations for sparse-reward downstream tasks.

Marco Pratticò, Pietro Novelli, Massimiliano Pontil, Carlo Ciliberto

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

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AI panel: 9 of 20 reviewers recommend it
lenient 4/5
medium 5/10
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80%Must read
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NeurIPS 2026U CambridgeExploration

Exploring Starts Are Not Enough: Counterexamples and a Fix for Monte Carlo Exploring Starts

Tabular Monte Carlo Exploring Starts can converge to suboptimal policies, but state-specific inverse-frequency learning-rate scaling restores convergence to optimality.

Octave Oliviers, Glenn Vinnicombe

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

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AI panel: 12 of 20 reviewers recommend it
lenient 2/5
medium 8/10
strict 2/5
74%Highly rated
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NeurIPS 2026OralFZI Research Center for InformatKarlsruhe Institute of TechnologKarlsruher Institut für TechnoloFZIExploration

CIG: Exploration via Conditional Information Gain

CIG derives a tractable trajectory-level information-gain reward via ensemble disagreement that conditions on replay buffers and rollout prefixes, outperforming prior methods across discrete and continuous exploration tasks.

Tim Joseph, Marcus Fechner, Philipp Stegmaier, Karam Daaboul and 1 more

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

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AI panel: 9 of 20 reviewers recommend it
lenient 2/5
medium 6/10
strict 1/5
86%Must read
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NeurIPS 2026OralTexas TechExploration

Emergence of Physical Intelligence via Controllable Information Production

Controllable Information Production grounds intrinsic motivation in dynamics and control, unifying them to measure controllable information production and outperforming prior methods on robot tasks.

Tristan Shah, Stas Tiomkin

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

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AI panel: 14 of 20 reviewers recommend it
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