Harness Continual Learning: Continual Adaptation Beyond Model Parameters
- Published
- Source
- arXiv
- Paper number
- 968
- Field
- Machine Learning
- arXiv ID
- 2608.19013
Key points
- It formalized the HCL paradigm, which shifts the target of continual learning from model parameters to the harness (prompts, memory, tools, and routing).
- It introduced 'harness forgetting' and controlled it through guarded evolution, which commits changes only after they pass checks for improvement, historical retention, and validity.
- Setting the historical-loss budget to 0 yielded average forgetting of 0.39, while leaving it unlimited yielded 3.45, allowing explicit control over the stability-plasticity tradeoff.
- It achieved relative improvements of at least 10% across 3 domains, text, multimodal, and open-world, with a highest overall multimodal score of 68.92%.
- Component-removal experiments showed that experience memory and task-interface updates contributed most to performance and retention.
Paper links
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