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News/SEARL: Joint Optimization of Policy and Tool Graph Memory for Self-Evolving Agents
arxiv
PublishedApril 10, 2026 at 4:00 AM
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SEARL: Joint Optimization of Policy and Tool Graph Memory for Self-Evolving Agents

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arXiv:2604.07791v1 Announce Type: cross Abstract: Recent advances in Reinforcement Learning with Verifiable Rewards (RLVR) have demonstrated significant potential in single-turn reasoning tasks. With the paradigm shift toward self-evolving agentic learning, models are increasingly expected to learn

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#reinforcement-learning#self-evolving-agents#knowledge-reasoning

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