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

Researchers introduce Ctx2Skill, a self-evolving framework that automatically discovers and refines natural-language skills for language models to better learn from complex contexts without manual annotation or external feedback. The system uses a multi-agent loop with a Challenger, Reasoner, and Judge to autonomously generate, test, and improve skills, showing consistent improvements across context learning benchmarks.

Ctx2Skill addresses a fundamental limitation in language model deployment: the inability to effectively leverage complex, technical contexts that exceed pre-training knowledge. Traditional approaches require prohibitively expensive manual skill annotation, creating a bottleneck for practical applications. The framework eliminates this constraint through autonomous skill discovery, enabling LMs to dynamically adapt to domain-specific reasoning tasks without human intervention.

The multi-agent self-play architecture represents an interesting departure from supervised fine-tuning paradigms. By embedding a Challenger-Reasoner-Judge loop, the system creates competitive pressure that drives skill refinement while the Cross-time Replay mechanism prevents degenerate solutions where agents exploit adversarial feedback rather than improving genuine reasoning capabilities. This approach mirrors game-theoretic learning dynamics seen in other AI systems, though applied to skill evolution.

For the broader AI development ecosystem, reducing annotation overhead has significant implications. As organizations deploy LMs in specialized domains—legal, medical, financial—the ability to automatically construct context-specific skills could accelerate adoption and reduce costs associated with prompt engineering and knowledge integration. The framework's agnostic design allows integration with any LM backbone, suggesting potential widespread applicability.

The real-world impact hinges on whether Ctx2Skill's improvements translate beyond benchmark scenarios. The evaluation on CL-bench demonstrates promise, but generalization to diverse industry verticals and complex reasoning chains remains to be demonstrated. Success here could shift how organizations approach knowledge augmentation for LMs, moving from manual curation toward automated skill synthesis.

• →Ctx2Skill autonomously discovers and refines language model skills without human annotation, reducing operational overhead for context learning applications.
• →A multi-agent self-play framework with Challenger, Reasoner, and Judge agents generates adversarial pressure that drives continuous skill improvement.
• →Cross-time Replay mechanism prevents adversarial collapse, ensuring evolved skills remain robust and generalizable across diverse tasks.
• →Skills generated by Ctx2Skill integrate with any language model backbone, enabling broader deployment across different model architectures.
• →Framework shows consistent performance improvements on context learning benchmarks, suggesting practical viability for domain-specific knowledge integration.