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arXiv AI By Zhezheng Hao, Yongkang Yang, Xilin Xia, Jiarui Yu, Yi Liu, Fanjunduo Wei, Peijie Dong, Tianfu Wang, Qiang Lin, Can Wang, Hande Dong, Jiawei Chen

Learning from Interaction Experience via Localized Context Distillation

Read the original on arXiv AI →

The paper introduces Localized Context Distillation (LoCD), a method that improves learning from interaction experience for large language models. LoCD identifies critical spans in a student’s trajectory that need revision, then has a teacher regenerate only those spans from the student’s own prefix, reducing exposure bias while focusing on informative states. Experiments across math reasoning, code generation, and agent tasks show that LoCD outperforms strong baselines and yields more stable training with smaller, sparser updates.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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