arXiv Machine Learning June 19, 2026 By Dillon Sandhu, Ronald Parr

Approximate Next Policy Sampling: Replacing Conservative Target Policy Updates in Deep RL

Read the original on arXiv Machine Learning →

arXiv:2605. 05481v2 Announce Type: replace Abstract: We revisit a classic "chicken-and-egg" problem in reinforcement learning: to safely improve a policy, the value function must be accurate on the state-visitation distribution of the updated policy.

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

arXiv Machine Learning

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Collaborative Weighting with Pessimistic Critic for Mitigating Overestimation in Off-Policy Reinforcement Learning

arXiv:2607. 26509v1 Announce Type: new Abstract: Deep off-policy reinforcement learning algorithms for continuous control typically rely on neural value function approximation to guide policy improvement.

By Gong Gao, Xiao Lai, Ziqi Xie, Guojie Chen, Xianhui Liu, Weidong Zhao

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Test-Time Gradient Guidance of Flow Policies in Reinforcement Learning

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An Agency-Transferring Model-Free Policy Enhancement Technique

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rag agents reinforcement-learning benchmarks

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Learning from Mistakes: Rollout-Retrieval Lifelong Policy Learning for Autonomous Driving

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By Cheng Gong, Haoyang Wang, Chao Lu, Zirui Li, Jianwei Gong

agents robotics fine-tuning benchmarks

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Parameter Exploration for RLVR via Variational Learning

arXiv:2608. 09805v1 Announce Type: cross Abstract: Exploration has been a focus of reinforcement learning research for a long time.

By Vatsal Venkatkrishna, Nico Daheim, Iryna Gurevych

llms reinforcement-learning

arXiv Machine Learning

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Policy Improvement Reinforcement Learning

arXiv:2604. 00860v3 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has become a central post-training paradigm for improving the reasoning capabilities of large language models.

By Huaiyang Wang, Xiaojie Li, Deqing Wang, Haoyi Zhou, Zixuan Huang, Yaodong Yang, Jianxin Li, Yikun Ban

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