arXiv Machine Learning June 30, 2026 By Dilxat Muhtar, Jiashun Liu, Wei Gao, Weixun Wang, Shaopan Xiong, Ju Huang, Siran Yang, Wenbo Su, Jiamang Wang, Ling Pan, Bo Zheng

Complementary RL: Towards Efficient Experience-Driven Agent Learning

Read the original on arXiv Machine Learning →

arXiv:2603. 17621v2 Announce Type: replace Abstract: Reinforcement Learning (RL) has emerged as a powerful paradigm for training LLM-based agents, yet remains limited by low sample efficiency, stemming not only from sparse outcome feedback but also from the agent's inability to leverage prior experience across episodes.

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

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Beyond Isolation: Unlocking Reinforcement Learning Component Synergy for Sample-Efficient Continuous Control

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