arxiv arXiv cs.CL · just now · research

SkillGym internalizes human skills into LLMs for real-world problem solving

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Researchers introduce SkillGym, a framework that transforms human-written agent skills into executable, verifiable training environments for large language model agents. The system utilizes a skill-to-task pipeline to instantiate concrete tasks and verify outcomes with code-based checkers.

The team constructs and releases 2,756 environments across 12 categories.
They collect 8,364 successful trajectories averaging 49 tool calls and over 60k logged text tokens.
Supervised fine-tuning on verified workflows improves Qwen3.5-35B-A3B by 199 Elo on GDPval-AA v2.
The resulting 35B SkillGym-Agent reaches 51.47% on skill-assisted SkillsBench, exceeding scores for Claude Sonnet 4.6 and GPT-5.4 Mini.

This approach enables the internalization of reusable procedural competence, allowing models to solve real-world problems more effectively than using external inference-time instructions.

Importance 2/3 Beats a top-lab benchmark New harness with differentiators arXiv cs.CL Anthropic Alibaba (Qwen) AI agents Training methods

Benchmarks

Benchmark	Model	Score
SWE-Lancer	SkillGym-Agent	51.47%
Terminal-Bench 2	Qwen3.5-35B-A3B	19.1%

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