From Context to Skills: Can Language Models Learn from Context Skillfully?

• URL: http://arxiv.org/abs/2604.27660v2
• Date: Sun, 03 May 2026 14:10:15 GMT
• Title: From Context to Skills: Can Language Models Learn from Context Skillfully?
• Abstract summary: 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.
• Score: 56.5857437218136
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many real-world tasks require language models (LMs) to reason over complex contexts that exceed their parametric knowledge. This calls for context learning, where LMs directly learn relevant knowledge from the given context. An intuitive solution is inference-time skill augmentation: extracting the rules and procedures from context into natural-language skills. However, constructing such skills for context learning scenarios faces two challenges: the prohibitive cost of manual skill annotation for long, technically dense contexts, and the lack of external feedback for automated skill construction. In this paper, we propose Ctx2Skill, a self-evolving framework that autonomously discovers, refines, and selects context-specific skills without human supervision or external feedback. At its core, a multi-agent self-play loop has a Challenger that generates probing tasks and rubrics, a Reasoner that attempts to solve them guided by an evolving skill set, and a neutral Judge that provides binary feedback. Crucially, both the Challenger and the Reasoner evolve through accumulated skills: dedicated Proposer and Generator agents analyze failure cases and synthesize them into targeted skill updates for both sides, enabling automated skill discovery and refinement. To prevent adversarial collapse caused by increasingly extreme task generation and over-specialized skill accumulation, we further introduce a Cross-time Replay mechanism that identifies the skill set achieving the best balance across representative cases for the Reasoner side, ensuring robust and generalizable skill evolution. The resulting skills can be plugged into any language model to obtain better context learning capability. Evaluated on four context learning tasks from CL-bench, Ctx2Skill consistently improves solving rates across backbone models.

Related papers

• Skill Self-Play: Pushing the Frontier of LLM Capability with Co-Evolving Skills [53.15216425100295]
  We introduce Skill Self-Play (Skill-SP), a co-evolutionary framework comprising a proposer, a solver, and a skill controller.<n>The proposer generates challenging tasks conditioned on dynamically sampled skills; the solver explores candidate solutions to push its capability boundaries; and the skill controller collects execution feedback to update and expand the skill library.<n>This interactive co-evolution effectively bridges the gap between structured verification and open-ended exploration.
  arXiv  Detail & Related papers  (2026-07-24T17:59:22Z)
• Co-Evolving Skill Generation and Policy Optimization [35.41582114275514]
  Existing methods typically use strong language models to analyze trajectories, generate skills, and update a retrievable skill bank during online training.<n>We propose an online reinforcement learning framework for pre-storage skill validation.
  arXiv  Detail & Related papers  (2026-06-07T17:55:55Z)
• SkillComposer: Learning to Evolve Agent Skills for Specification and Generalization [99.18305770138677]
  Agent skills are reusable strategies that guide agent reasoning and action.<n>Current skill construction methods treat the problem as one-shot extraction.<n>We introduce SkillComposer, a framework that decomposes skill construction into three learnable operations.
  arXiv  Detail & Related papers  (2026-06-04T12:16:08Z)
• 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)
• Skill Neologisms: Towards Skill-based Continual Learning [46.44104273347049]
  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.
  arXiv  Detail & Related papers  (2026-05-06T14:27:12Z)
• SkillForge: Forging Domain-Specific, Self-Evolving Agent Skills in Cloud Technical Support [3.277125886726497]
  Existing skill creators lack domain grounding, producing skills poorly aligned with real-world task requirements.<n>We introduce SkillForge, a self-evolving framework that closes the end-to-end creation-refinement loop.<n>We evaluate five real-world cloud support scenarios spanning 1,883 tickets and 3,737 tasks.
  arXiv  Detail & Related papers  (2026-04-09T07:11:23Z)
• 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)
• EvoSkills: Self-Evolving Agent Skills via Co-Evolutionary Verification [85.3692584167951]
  Anthropic proposes the concept of skills for LLM agents to tackle multi-step professional tasks.<n>A tool is a single, self-contained function, whereas a skill is a structured bundle of interdependent multi-file artifacts.<n>EvoSkills is a self-evolving skills framework that enables agents to autonomously construct complex, multi-file skill packages.
  arXiv  Detail & Related papers  (2026-04-02T06:43:20Z)
• 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)
• Agentic Skill Discovery [19.5703917813767]
  Language-conditioned robotic skills make it possible to apply the high-level reasoning of Large Language Models (LLMs) to low-level robotic control. A remaining challenge is to acquire a diverse set of fundamental skills. We introduce a novel framework for skill discovery that is entirely driven by LLMs.
  arXiv  Detail & Related papers  (2024-05-23T19:44:03Z)
• Rethinking Mutual Information for Language Conditioned Skill Discovery on Imitation Learning [36.624923972563415]
  We propose an end-to-end imitation learning approach known as Language Conditioned Skill Discovery (LCSD) We utilize vector quantization to learn discrete latent skills and leverage skill sequences of trajectories to reconstruct high-level semantic instructions. Our approach exhibits enhanced generalization capabilities towards unseen tasks, improved skill interpretability, and notably higher rates of task completion success.
  arXiv  Detail & Related papers  (2024-02-27T13:53:52Z)
• SkillDiffuser: Interpretable Hierarchical Planning via Skill Abstractions in Diffusion-Based Task Execution [75.2573501625811]
  Diffusion models have demonstrated strong potential for robotic trajectory planning. generating coherent trajectories from high-level instructions remains challenging. We propose SkillDiffuser, an end-to-end hierarchical planning framework.
  arXiv  Detail & Related papers  (2023-12-18T18:16:52Z)
• LISA: Learning Interpretable Skill Abstractions from Language [85.20587800593293]
  We propose a hierarchical imitation learning framework that can learn diverse, interpretable skills from language-conditioned demonstrations. Our method demonstrates a more natural way to condition on language in sequential decision-making problems.
  arXiv  Detail & Related papers  (2022-02-28T19:43:24Z)

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.