Sample-Efficient Learning from Agent Experience
- Published
- Source
- arXiv
- Paper number
- 716
- Field
- LLMs / NLP
- arXiv ID
- 2607.21051
Key points
- The paper defines the problem of Experience Distillation, which distills an agent's interaction experience into weights, and proposes a solution.
- It removes extra environment use by using a one-step branch rollout that resamples only the teacher's next action at each point in the collected trajectory.
- It preserves 64.8 percent of the in-context learning effect, which greatly outperforms standard SFT at 3.8 percent recovery.
- It matches reinforcement-learning baselines while using more than 9.6 times fewer environment samples.
- The experimental details, including experience preprocessing, strengthened teacher inference, and branch packing, are validated empirically.
Paper links
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