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Sample-Efficient Learning from Agent Experience

Research LLM Agents

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Overall 77
Content 80
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Merged summary

TL;DR - Experience Distillation transfers agents’ in-context learning from interaction histories into model weights without additional environment interactions. It retains most in-context gains while using far fewer samples than reinforcement-learning baselines.

  • Retains at least 64.8% of in-context learning gains across software-engineering tasks and text-adventure games.
  • Direct supervised fine-tuning recovers only 3.8% of those gains.
  • Evaluated on 749 curated software-engineering tasks and six text-adventure games.
  • Matches classical reinforcement-learning baselines with at least 9.6Ă— fewer environment samples.

Sources (1)

Sample-Efficient Learning from Agent Experience

arXiv cs.CL Chenhui Gou, Haoqin Tu, Yunhao Fang, Jianfei Cai, Hamid Rezatofighi 2026-07-23 arXiv:2607.21051
Public signals Hugging Face upvotes 20
Providers: Hugging Face · Upvotes 20 OpenAlex · N/A Publisher · N/A Semantic Scholar · N/A X · N/A Fetched 2026-08-24 14:35:26.405587 UTC

TL;DR - Experience Distillation transfers agents’ in-context learning from interaction histories into model weights without additional environment interactions. It retains most in-context gains while using far fewer samples than reinforcement-learning baselines.

  • Retains at least 64.8% of in-context learning gains across software-engineering tasks and text-adventure games.
  • Direct supervised fine-tuning recovers only 3.8% of those gains.
  • Evaluated on 749 curated software-engineering tasks and six text-adventure games.
  • Matches classical reinforcement-learning baselines with at least 9.6Ă— fewer environment samples.
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