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arXivImitation learning

Iterative Policy Refinement through Semantic Rollout Analysis

A closed-loop framework iteratively refines structured imitation-learning policies via LLM analysis of rollout tables, improving performance by up to 15% and cutting compute 75%.

Feiyu Gavin Zhu, Qi Xu, Zhifei Deng, Zhigang Hua and 3 more

Published Oct 1, 2026 · 0 citations

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NeurIPS 2026U Chinese Academy of SciencesXiaohongshu IncU the Chinese Academy of ScienceSCIRInstitute of Software, Chinese AImitation learning

Your Teacher Can’t Help You Here: Combating Supervision Fidelity Decay in On-Policy Distillation

Yanjiang Liu, Jie Lou, Xinyan Guan, Yuqiu Ji and 6 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 Southern CaliforniaImitation learning

Reinforcement Learning from Rich Feedback with Distributional DAgger

Rishabh Agrawal, Jacob Fein-Ashley, Paria Rashidinejad

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

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NeurIPS 2026ÖrebroÖrebro University, SwedenImitation learning

RAIL: Representation-Aligned Imitation Learning for Student-Compatible Teacher Policies

Meraj Mammadov, Pedro Zuidberg Dos Martires, Johannes A. Stork

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

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NeurIPS 2026Technische Universität DarmstadtGerman Research Center for AITU DarmstadtImitation learning

A Geometric Perspective on Reward Function Updates in Inverse Reinforcement Learning

Anish Abhijit Diwan, Jan Peters, Oleg Arenz

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

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NeurIPS 2026Washington University in St. LouMicrosoft ResearchImitation learning

Skill-Level Effects in Behavioral Cloning: When Low-Skill Data Improves Performance

Saumik Narayanan, Kassa Korley, Siddhartha Sen, Chien-Ju Ho

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

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NeurIPS 2026ETH AI Center, ZurichU California BerkeleyUIUCEPFLU California, BerkeleyImitation learning

When Does Interaction Help? Representational Tradeoffs in Value-Based Imitation Learning

Luca Viano, Antoine Moulin, Audrey Huang, Volkan Cevher 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 2026U Illinois at ChicagoImitation learning

Deployment-Time Online Imitation Learning from Corrective Demonstrations

Saleh Momeni, Bing Liu

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

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NeurIPS 2026Istituto Italiano di TecnologiaGenerative BionicsU VeronaImitation learning

HIDRA: Hierarchical Dual-Routing Attention for Replay-Free Lifelong Imitation Learning

Fanqi Yu, Matteo Tiezzi, Cigdem Beyan, Tommaso Apicella 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 2026NanjingHuaweiShanghai Jiao TongImitation learning

DiReCL: Learning Differentiable Reward Code with Inverse Reinforcement Learning

Aoran Wang, Jingtao Zhang, Maosen Li, Zongzhang Zhang

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

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NeurIPS 2026Shanghai JiaotongShanghai Jiao TongImitation learning

Rectified Policy Rollouts with Hierarchical Expert Guidance for Neural Combinatorial Optimization

Wenzheng Pan, Nuoyan Chen, Jiaxi Liu, Jiale Ma 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 2026Korea Institute of Science and TKAISTYonseiKorea Institute for Advanced StuImitation learning

Reconciling Safety and Performance via Dual-Expert Offline Imitation Learning

Seokin Seo, Sung Kuk Shyn, Minji Seo, Yisoo Lee and 3 more

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

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72%Highly rated
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NeurIPS 2026U North Carolina at Chapel HillImitation learning

Imitation from Observations with Trajectory-Level Generative Embeddings

TGE constructs smooth trajectory-level diffusion embeddings to estimate expert state density for robust offline imitation from observations with scarce expert data.

Yongtao Qu, Shangzhe Li, Weitong Zhang

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

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lenient 3/5
medium 5/10
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83%Must read
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NeurIPS 2026Eberhard-Karls-Universität TübinETH AI Center, ZurichEPFLU TuebingenImitation learning

Split the Differences, Pool the Rest: Provably Efficient Multi-Objective Imitation

MA-BC partitions conflicting expert trajectories while pooling compatible data to recover Pareto-optimal policies in multi-objective imitation with minimax optimal rates.

Ziyad Sheebaelhamd, Luca Viano, Volkan Cevher, Claire Vernade

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

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lenient 3/5
medium 8/10
strict 2/5
78%Highly rated
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NeurIPS 2026U WashingtonNetflix & CornellNetflixImitation learning

Inverse Reinforcement Learning with Just Classification and a Few Regressions

GenPQR reduces inverse reinforcement learning to policy estimation via classification followed by Q-function regression, yielding modular finite-sample guarantees and improved reward recovery across continuous action spaces.

Lars van der Laan, Nathan Kallus, Aurelien Bibaut

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

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AI panel: 11 of 20 reviewers recommend it
lenient 3/5
medium 6/10
strict 2/5
74%Highly rated
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NeurIPS 2026U Southern CaliforniaUSC / Google DeepMindImitation learning

When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited

Robust minimax task inference for behavior foundation models enables offline imitation learning robust to dynamics shifts using only single-environment data, outperforming standard and robust baselines.

Rishabh Agrawal, Rahul Jain, Ashutosh Nayyar

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026 · ▲ 1 on Hugging Face

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lenient 5/5
medium 4/10
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80%Must read
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NeurIPS 2026U PennsylvanniaU Pennsylvania, University of PeColumbiaImitation learning

Learning When to Stop: Selective Imitation Learning Under Arbitrary Dynamics Shift

Selective imitation with SeqRejectron learns when to stop under arbitrary dynamics shifts, yielding horizon-free sample complexity via validator-based stopping rules with completeness and soundness guarantees.

Surbhi Goel, Jonathan Pei, James Wang

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

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AI panel: 12 of 20 reviewers recommend it
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