Learning From Your Own Traces

The text discusses the challenges and methodologies involved in training machine learning models to retain new skills while preserving existing knowledge. It highlights the limitations of current practices where session data is discarded, leading to missed opportunities for improving models through the integration of post-deployment experiences. The document elaborates on various approaches, such as provenance masking and the use of cartridges, to enhance learning and skill retention. Provenance masking helps in selectively training models by excluding failed attempts, improving repair task success rates significantly. Cartridges are used to store discrete capabilities in models without interfering with existing skills, although they face limitations in retaining more than a few facts. The text also explores routing challenges within the system, where explicit tagging and routing mechanisms are necessary to optimize task-solving capabilities. The experiments highlight the importance of obtaining initial successes through external demonstrations to transition from failure to success, underscoring the need for a structured approach in updating machine learning models to balance the integration of new skills with the retention of existing ones.

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