🧠 From "reading context" to "skillfully learning from it." This method has an LLM acquire context-specific skills through self-play alone, with no human annotation and no external feedback. Title: From Context to Skills: Can Language Models Learn from Context Skillfully? URL: 📝 Overview LLMs are strong on knowledge seen in pretraining but weak on novel, specialized contexts. This paper proposes Ctx2Skill, which autonomously discovers and refines context-specific skills without human annotation or external feedback. ❓ Challenges Solved Annotating long, technically dense documents is prohibitively costly. And unlike coding, context learning has no execution feedback to verify, making automated skill construction hard. 💡 Methodology & Proposed Approach ・A multi-agent self-play of five frozen-LM roles iterates N=5 times over M=5 tasks ・A Challenger creates tasks and rubrics probing weaknesses, a Reasoner solves them, and a Judge gives pass/fail verdicts ・Proposer and Generator pairs diagnose failures and synthesize skill updates ・A Cross-Time Replay mechanism maximizes the product of hard- and easy-probe performance to pick the most generalizable skill set across iterations 🎯 Use Cases It fits feeding a model long specialized documents and having it acquire the needed skills on the spot, directly useful where you need rapid adaptation to domain-specific knowledge. 📊 Experimental Results ・On CL-Bench (500 contexts, 1,899 tasks), GPT-4.1's solving rate rose from 11.1% to 16.5% ・GPT-5.1 went from 21.1% to 25.8%, and GPT-5.2 from 18.2% to 21.4% ・Skills transfer from stronger to weaker models: GPT-5.1's skills on GPT-4.1 give 16.1% ・The augmented GPT-4.1 (16.5%) beats an unaugmented Gemini 3 Pro (15.8%) #LLM# #InContextLearning#