Zhilong Ge

SkillGym introduces a framework that transforms human-written agent skills into executable, verifiable training environments: its skill-to-task pipeline instantiates concrete tasks, verifies outcomes with code-based checkers, and assesses empirical skill dependence through contrastive executions; it constructs and releases 2,756 environments across 12 categories and collects 8,364 successful trajectories (averaging 49 tool calls and over 60k logged text tokens), supporting supervised fine-tuning on verified workflows and reinforcement learning with outcome-based rewards; under Claude Code, supervised fine-tuning improves Qwen3.5-35B-A3B by 199 Elo on GDPval-AA v2, 19.10 percentage points on Terminal-Bench 2.1, and 28.13 and 12.38 points on SkillsBench v1.

SkillGym introduces a framework that transforms human-written agent skills into executable, verifiable training environments: its skill-to-task pipeline instantiates concrete tasks, verifies outcomes with code-based checkers, and assesses empirical skill dependence through contrastive executions; it constructs and releases 2,756 environments across 12 categories and collects 8,364 successful trajectories (averaging 49 tool calls and over 60k logged text tokens), supporting supervised fine-tuning on verified workflows and reinforcement learning with outcome-based rewards; under Claude Code, supervised fine-tuning improves Qwen3.5-35B-A3B by 199 Elo on GDPval-AA v2, 19.10 percentage points on Terminal-Bench 2.1, and 28.13 and 12.38 points on SkillsBench v1.