When Skills Remain But Access Fails: Factorized Skill Memory for Continual Skill Internalization
Skill internalization aims to convert externally specified procedural skills into persistent model capabilities, allowing language-model agents to execute them without retrieving or reinjecting skill descriptions at inference time. Yet when new skills are learned continually, whether previously internalized skills remain usable is still poorly understood. We study forgetting in continual skill internalization and uncover a clear hierarchical structure. Early in subsequent training, a model may lose the ability to invoke an old skill from the raw request, while supplying only the correct canonical skill name substantially restores the learned behavior; with further optimization, this condi...
| Authors | Haomiao Qiu, Yining Tang, Hongyi Wan et al. |
|---|---|
| Primary category | cs.AI |
| Published | 11 October 2026 |
| Last updated at source | 11 October 2026 |
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