Skill Neologisms: Towards Skill-based Continual Learning
Quick summary
arXiv:2605.04970v3 Announce Type: replace-cross Abstract: Modern LLMs show mastery over an ever-growing range of skills, as well as the ability to compose them flexibly. However, extending model capabilities to new skills in a scalable manner is an open problem: fine-tuning and parameter-efficient variants risk catastrophic forgetting, while context-based approaches have limited expressiveness and are constrained by the model's effective context. We explore skill neologisms--soft tokens integrated in the model's vocabulary and optimized to improve capabilities over a specific skill--as a way t
Key takeaways
- arXiv:2605.04970v3 Announce Type: replace-cross Abstract: Modern LLMs show mastery over an ever-growing range of skills, as well as the ability to compose them flexibly.
- However, extending model capabilities to new skills in a scalable manner is an open problem: fine-tuning and parameter-efficient variants risk catastrophic forgetting, while context-based approaches have limited expressiveness and are constrained by the model's effective context.
- We explore skill neologisms--soft tokens integrated in the model's vocabulary and optimized to improve capabilities over a specific skill--as a way t
Why it matters
“Skill Neologisms: Towards Skill-based Continual Learning” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.
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