SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning

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 its sparse trajectory-level rewards offer limited guidance on intermediate decisions, leaving a supervision gap between episode-level outcomes and token-level policy learning. We propose SEED (SElf-Evolving On-Policy Distillation), a self-evolving

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ethics.ai (15 July 2026), “SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning,” evidence record 10995, https://ethics.ai/record/10995 (originally published by HuggingFace Daily Papers).

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