RetireOPD: Self-Retiring On-Policy Distillation for Agentic Reinforcement Learning
- Published
- Source
- arXiv
- Paper number
- 1103
- Field
- AI Agents
- arXiv ID
- 2609.20784
Key points
- Self-retiring mechanism dynamically removes teacher supervision as student policy matures
- Prevents persistent distillation bias improving both sample efficiency and final performance
- Applicable to multi-turn RL agent self-improvement loop design
- Enables agents to decide when to become independent from the teacher — a meta-learning structure for self-improvement loops
Paper links
External research summaries. These are not HDATF publications or measured product results.