Large language models often need to keep improving after they are deployed, and learning from interaction experience is one way to do that. A new arXiv paper, “Learning from Interaction Experience via Localized Context Distillation,” explores this problem. The authors argue that context distillation is a promising approach: it extracts privileged information from experience and uses it to guide the model.
The paper specifically proposes a localized version of context distillation. While the abstract does not elaborate on what “localized” means in this setting, the title and framing suggest a targeted or region-specific distillation process. The work appears to aim at making the learning process more efficient or focused during real-world use.
Because the abstract is brief, the exact mechanism and experimental results are not described in the source. Still, the paper signals continued interest in using deployment-time interactions to refine LLM behavior, building on the broader idea of distillation as a tool for continual learning.