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Sample-Efficient Learning from Agent Experience

7 upvotes · 23 JUL 2026 · Chenhui Gou, Haoqin Tu, Yunhao Fang et al.

This paper develops a method called Experience Distillation that allows agents to learn from their own interaction histories without needing additional environment interactions, making learning more sample-efficient. Practitioners might care about this because it can improve the performance of agents in complex environments with limited resources.

continual learningin-context learningreinforcement-learningagent experiencesample efficiency