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News/SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning
arxiv
PublishedJuly 17, 2026 at 4:00 AM
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SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning

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arXiv:2607.14777v1 Announce Type: new Abstract: Large language models are increasingly trained as interactive agents for long-horizon tasks involving multi-turn interaction, tool use, and environment feedback. Outcome-based reinforcement learning (RL) provides a practical optimization paradigm, but

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