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On October 30, 2026, Rohan Paul published an article introducing a new paper from Princeton and Meta on the continuous improvement of AI agents. The paper proposes the Self-Direction method: it has failed agents reflect on the current run, trains the lessons into LoRA adapters about 0.02% the size of the model, and then a router loads the closest adapter when a similar failure is encountered. On Qwen3.6-27B, the GAIA task success rate rose from 38.8% to 55.8%, outperforming in-context reflection and the Skills.md file approach; among 185 errors that had occurred previously, 67.3% of fixes were still effective by the third encounter, compared with 42.9% for simple retries. There have been no subsequent developments to this introduction so far.
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Oct 30
Rohan Paul
Princeton and Meta propose Self-Direction: storing agent error-correction experience as reusable LoRA adapters
A new paper from Princeton and Meta proposes the Self-Direction method, which lets a failed agent reflect on the current run and trains the lessons into a LoRA adapter about 0.02% the size of the model; a router then loads the closest adapter when a similar failure is encountered. On Qwen3.6-27B, the GAIA task success rate rises from 38.8% to 55.8%, outperforming in-context reflection and the Skills.md file approach. Of 185 errors that had occurred previously, 67.3% of fixes were still effective by the third encounter, versus 42.9% for simple retries.