Skill Neologisms: Towards Skill-based Continual Learning

• URL: http://arxiv.org/abs/2605.04970v1
• Date: Wed, 06 May 2026 14:27:12 GMT
• Title: Skill Neologisms: Towards Skill-based Continual Learning
• Abstract summary: We show that skill neologisms can be learned to improve model capabilities on specific skills while being composable with out-of-distribution skills.<n>These results suggest that skill neologisms may provide a scalable path towards skill-based continual learning.
• Score: 46.44104273347049
• License: http://creativecommons.org/licenses/by/4.0/
• 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--i.e., soft tokens integrated in the model's vocabulary and optimized to improve capabilities over a specific skill--as a way to selectively extend model capabilities to new skills without weight updates. We first observe that off-the-shelf pre-trained LLMs already demonstrate tokens associated with procedural knowledge. We then show that skill neologisms can be learned to improve model capabilities on specific skills while being composable with out-of-distribution skills, and that independently trained skill neologisms can be composed zero-shot. These results suggest that skill neologisms may provide a scalable path towards skill-based continual learning.

Related papers

• ContinualSkillBench: Can LLM Agents Truly Evolve Their Capabilities? [42.712923465058786]
  We introduce ContinualSkillBench, a dynamic evaluation framework for in-context continual skill learning.<n>We show that sequential execution generally improves performance, but the gains vary substantially across models and domains.<n>We further find that less capable models tend to accumulate larger, more fragmented collections of task-specific skills.
  arXiv  Detail & Related papers  (2026-08-04T16:15:02Z)
• Skill-R1: Agent Skill Evolution via Reinforcement Learning [84.35984979949502]
  Skill-R1 is a reinforcement learning framework for instance-level recurrent skill optimization from verifiable rewards.<n>It preserves black-box compatibility with both open- and closed-source models while making adaptation substantially cheaper than model-level updates.
  arXiv  Detail & Related papers  (2026-05-10T06:19:15Z)
• From Context to Skills: Can Language Models Learn from Context Skillfully? [56.5857437218136]
  Ctx2Skill is a self-evolving framework that autonomously discovers, refines, and selects context-specific skills.<n>It can be plugged into any language model to obtain better context learning capability.
  arXiv  Detail & Related papers  (2026-04-30T09:53:15Z)
• SKILL0: In-Context Agentic Reinforcement Learning for Skill Internalization [61.37478254738943]
  Inference-time skill augmentation is fundamentally limited.<n>We introduce SKILL0, an in-context reinforcement learning framework designed for skill internalization.<n>SKILL0 achieves substantial improvements over the standard RL baseline.
  arXiv  Detail & Related papers  (2026-04-02T17:03:05Z)
• Lifelong Language-Conditioned Robotic Manipulation Learning [19.640584848758625]
  Traditional language-conditioned manipulation agent sequential adaptation to new manipulation skills leads to catastrophic forgetting of old skills.<n>We propose SkillsCrafter, a novel robotic manipulation framework designed to continually learn multiple skills.
  arXiv  Detail & Related papers  (2026-03-05T13:30:33Z)
• Pretrained Bayesian Non-parametric Knowledge Prior in Robotic Long-Horizon Reinforcement Learning [10.598207472087578]
  Reinforcement learning (RL) methods typically learn new tasks from scratch, often disregarding prior knowledge that could accelerate the learning process.<n>This work introduces a method that models potential primitive skill motions as having non-parametric properties with an unknown number of underlying features.<n>We utilize a non-parametric model, specifically Dirichlet Process Mixtures, enhanced with birth and merge, to pre-train a skill prior that effectively captures the diverse nature of skills.
  arXiv  Detail & Related papers  (2025-03-27T20:43:36Z)
• MaestroMotif: Skill Design from Artificial Intelligence Feedback [67.17724089381056]
  MaestroMotif is a method for AI-assisted skill design, which yields high-performing and adaptable agents.<n>We present MaestroMotif, a method for AI-assisted skill design, which yields high-performing and adaptable agents.
  arXiv  Detail & Related papers  (2024-12-11T16:59:31Z)
• C$\cdot$ASE: Learning Conditional Adversarial Skill Embeddings for Physics-based Characters [49.83342243500835]
  We present C$cdot$ASE, an efficient framework that learns conditional Adversarial Skill Embeddings for physics-based characters. C$cdot$ASE divides the heterogeneous skill motions into distinct subsets containing homogeneous samples for training a low-level conditional model. The skill-conditioned imitation learning naturally offers explicit control over the character's skills after training.
  arXiv  Detail & Related papers  (2023-09-20T14:34:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.