arXiv Machine Learning August 6, 2026 By Zheyuan Zhang, Manqing Mao, Hong Wang, Zhuoer Wang, Samson Koelle, Jie Yuan, Yanjun Lin, James Feng, Nikki Lijing Kuang, Yanfang Ye, Wei Niu

Optimizing What Policies Learn From: Recoverability-aware Rollout Intervention Learning

Read the original on arXiv Machine Learning →

arXiv:2608. 05080v1 Announce Type: new Abstract: Critic-free group-based reinforcement learning has become a scalable approach for post-training large language models.

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

arXiv AI

Jul 24

PATS: Policy-Aware Training Scaffolding for Agentic Reinforcement Learning

arXiv:2607. 21419v1 Announce Type: new Abstract: In long-horizon LLM agent reinforcement learning, weak policies often repeat similar failures, producing uninformative rollout trajectories and limiting effective policy optimization.

By Yipeng Shi, Zhipeng Ma, Yue Wang, Qitai Tan, Yang Li, Peng Chen, Zhengzhou Zhu

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arXiv AI

Jul 8

Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agents

arXiv:2607. 06223v1 Announce Type: new Abstract: Reinforcement learning has become a promising paradigm for improving large language model (LLM) agents on long-horizon search tasks, where the agent must make a sequence of intermediate decisions before receiving a final outcome.

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arXiv AI

6d ago

Retry, Switch, or Abstain? Learning Strategy-Aware Tool-Use Policies via Controlled Error Injection

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arXiv Machine Learning

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arXiv AI

Jul 7

Progress- and Reliability-Oriented Group Policy Optimization for Agentic Reinforcement Learning

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By Mingxuan Fan, Peiyang Liu

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arXiv AI

Jun 30

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

arXiv:2606. 30537v1 Announce Type: cross Abstract: Autonomous driving policies should be able to improve continually as deployment exposes them to increasingly diverse and long-tail traffic situations.

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

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