Benchmark RadarSkillEvolBench
SkillEvolBench evaluates whether LLM agents can distill episodic experience into reusable procedural skills. It includes 180 tasks across six environments, with acquisition tasks and frozen deployment tasks testing context shift, adversarial shortcuts, and composition. Scoring uses success rates and other agent metrics.
- Released
- 2026-05-22
- Readiness
- Paper only
- Primary field
- General AI
Why it matters
It is unclear whether LLM agents can form durable procedural skills from experience. SkillEvolBench provides a diagnostic testbed comparing skill-based learning against raw-trajectory reuse, informing agent design and training.
Motivation
Large language model (LLM) agents accumulate rich episodic trajectories while solving real-world tasks, but it remains unclear whether such experience can be distilled into reusable procedural skills.
Primary resources
Benchmark Radar records only publicly supported details and links back to primary sources for verification.