arXiv AI June 2, 2026 By Zhongyu He, Yuanfan Li, Fei Huang, Tianyu Chen, Siyuan Chen, Xingyang Li, Meng Hsuan Yu, Xiangrong Liu, Leyi Wei, Lu Pan, Ke Zeng, Xunliang Cai

SIRI: Self-Internalizing Reinforcement Learning with Intrinsic Skills for LLM Agent Training

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arXiv:2606. 02355v1 Announce Type: new Abstract: Long-horizon LLM agents can benefit from reusable skills, yet existing skill-based methods often rely on external skill generators during training or persistent skill retrieval at inference, increasing engineering complexity, context length, and deployment latency.

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

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Bidirectional Context Self-Distillation for Reinforcement Learning of Skill-Based LLM Agents

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Enabling agents to learn from experience and internalize it into their policy has become a central problem in self-evolving AI. On-policy self-distillation (OPSD) offers an effective pathway by using a privileged self-teacher to provide dense supervision on the student's own trajectories; however, existing methods still rely heavily on designer-specified privileged artifacts (e.

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Agentic Reinforcement Learning with Self-Distilled Reward Shaping

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