The authors developed RetireOPD for multi-turn agent training, where reinforcement learning ordinarily provides only one scalar reward for an entire trajectory.
Self-retiring distillation improves reinforcement learning for multi-turn agents
RetireOPD uses a skill-conditioned teacher for dense supervision, then automatically retires it when the student is ready to continue with reinforcement learning alone.
Chinese Tech
Yan Yu · Zhengxi Lu · Yizhou Liu · Yichen Pan · Aozhe Wang · Qipeng Chen · +5 more
Zhejiang University · Alibaba Group
Research Digest··2 min read
The authors train a privileged teacher using environment rewards, then jointly train a skill-free student through reinforcement learning and token-level on-policy distillation.
Why this paper
From Alibaba Group and Zhejiang University · Released code · Part of Credit Assignment in Agentic RL, now 17 papers
In one line
RetireOPD adaptively removes teacher supervision during agentic RL when it no longer benefits the student.
What it released
Code
What we could check
- ✓Code link in the paper (github.com)
- ·No weights link found
- ·No dataset link found
- ·No compute details found
- ·No stated limitations found
- ·No benchmark numbers found
Observed from the paper text and links we have. Absence here means we did not find it, not that it does not exist.
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