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2:15
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Appsofa
RetireOPD: Self-Retiring On-Policy Distillation for Agentic Reinforcement Learning
Training language-model agents with reinforcement learning is hard because they usually get only one reward at the end of a long task, which makes learning slow and unstable. This paper tackles that problem with RetireOPD, a method that adds a self-teaching signal during training. The key idea is to first train a skill-conditioned teacher that ...
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