arXiv Machine Learning June 2, 2026 By Volodymyr Tkachuk, Csaba Szepesv\'ari, Xiaoqi Tan

Trajectory Data Suffices for Statistically Efficient Policy Evaluation in Fixed-Horizon Offline RL with Linear $q^\pi$-Realizability and Concentrability

Read the original on arXiv Machine Learning →

arXiv:2510. 03494v2 Announce Type: replace Abstract: We study finite-horizon offline reinforcement learning (RL) with function approximation for both policy evaluation and policy optimization.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

reinforcement-learning

arXiv Machine Learning

Jun 30

Theoretical Foundations and Effective Algorithms for Policy-Aware Simulator Learning

arXiv:2605. 29032v2 Announce Type: replace Abstract: Model-based reinforcement learning (MBRL) agents typically learn world models by minimizing predictive loss.

By Christoph Dann, Yishay Mansour, Mehryar Mohri

agents reinforcement-learning safety

arXiv Machine Learning

Jun 18

When Does Trajectory-Level Supervision Permit Efficient Offline Reinforcement Learning?

arXiv:2606. 18531v1 Announce Type: cross Abstract: Offline reinforcement learning is typically analyzed under process-level reward supervision, yet many sequential decision datasets record only trajectory-level outcomes.

By Xuanfei Ren, Tengyang Xie

reinforcement-learning

arXiv Machine Learning

Jul 21

Theoretical Foundations of $\max$@$k$ Reinforcement Learning

arXiv:2607. 17823v1 Announce Type: new Abstract: Reinforcement Learning is a cornerstone technique for modern large reasoning models.

By Riccardo Poiani, Martino Bernasconi, Andrea Celli

agents reinforcement-learning

arXiv AI

Jun 9

On the Complexity of Offline Reinforcement Learning with $Q^\star$-Approximation and Partial Coverage

arXiv:2602. 12107v2 Announce Type: replace-cross Abstract: We study offline reinforcement learning under $Q^\star$-approximation and partial coverage, a setting that motivates practical algorithms such as Conservative $Q$-Learning (CQL; Kumar et al.

By Haolin Liu, Braham Snyder, Chen-Yu Wei

reinforcement-learning

arXiv Machine Learning

Jun 2

Off-Policy Learning in Large Action Spaces: Optimization Matters More Than Estimation

arXiv:2509. 03456v2 Announce Type: replace-cross Abstract: Off-policy evaluation (OPE) and off-policy learning (OPL) are foundational for decision-making in offline contextual bandits.

By Imad Aouali, Otmane Sakhi

arXiv AI

Jun 10

Test-Time Gradient Guidance of Flow Policies in Reinforcement Learning

arXiv:2606. 11087v1 Announce Type: cross Abstract: Expressive continuous control policies, such as diffusion and flow models, form the backbone of recent advances in scaling imitation learning for simulated and real robot control.

By Zhiyuan Zhou, Andy Peng, Charles Xu, Qiyang Li, Tobias Springenberg, Kevin Frans, Sergey Levine

diffusion reinforcement-learning robotics benchmarks

Related stories

Theoretical Foundations and Effective Algorithms for Policy-Aware Simulator Learning

When Does Trajectory-Level Supervision Permit Efficient Offline Reinforcement Learning?

Theoretical Foundations of $\max$@$k$ Reinforcement Learning

On the Complexity of Offline Reinforcement Learning with $Q^\star$-Approximation and Partial Coverage

Off-Policy Learning in Large Action Spaces: Optimization Matters More Than Estimation

Test-Time Gradient Guidance of Flow Policies in Reinforcement Learning