Study proposes small adapters to preserve AI agents’ lessons from mistakesMachine translation

Nice new paper on continuous improvement of AI agents from Princeton and Meta Finds that agents repeat fewer mistakes when each fix is saved as a LoRA adapter and reused on similar tasks. Agents can fix a mistake within a session, then often repeat it in the next one. Notes in context get forgotten, and full fine-tuning is costly and can disrupt other skills. With Self-Direction, a failing agent reflects on its run and trains the lesson into a LoRA adapter about 0.02% of the model's size. A router later loads the closest adapter when the agent fails a similar task. With Qwen3.6-27B, task success on GAIA rose from 38.8% to 55.8%, beating in-context reflections and a Skills.md file. Across 185 earlier mistakes, 67.3% of repairs still worked by the third encounter, against 42.9% for plain retrying. If your agents keep making the same mistakes, save each fix as a small adapter they can reload, not only as prompt notes.