SkillSandbox creates new tasks to test and verify reusable AI skills
SkillSandbox: Skill Verification via Dynamic Scenario Synthesis
Artificial IntelligenceComputation and Language
Summary
AI systems build skills from past experiences to solve tasks faster later, but these skills might not always work well outside their original context. The authors propose SkillSandbox, a tool that creates new, customized tasks to test if these skills are truly useful in different situations. It compares how well a skill performs versus not using it, deciding if the skill should be kept or discarded. This approach helps improve AI’s ability to reuse skills effectively and efficiently in new problems.
What this means in practice
- •For ai engineers: Create tailored test environments that reveal whether a learned skill will help future AI tasks beyond training data.
- •For game developers: Generate novel in-game scenarios to verify and refine AI-controlled characters’ learned behaviors for robustness.
Authors
Serin Kim, Kwangwook Seo, Dokyung Song, Jinyoung Yeo, Dongha Lee
Abstract
Self-evolving agents distill task-solving experience into skills for future reuse, but these skills can encode incorrect procedures or non-transferable knowledge. It is therefore critical to verify each skill's reusability: whether its guidance remains useful beyond the experience from which it was distilled. Such verification requires observing how a skill affects execution in new tasks, yet existing tasks may not expose the situations where the target skill can actually be exercised. To construct such situations, we propose SkillSandbox, a framework that dynamically synthesizes a task and its environment for each skill that are skill-relevant yet novel. A Proposer specifies the conditions to preserve and the source-specific details to vary, a Builder constructs an executable scenario, and a Verifier compares executions with and without the skill. The Verifier assesses executability, utility, and efficiency to assign a Keep or Reject verdict, determining whether the skill enters the library. Across ALFWorld and WebShop with three models, SkillSandbox consistently yields the strongest downstream performance and improved execution efficiency. Further analyses examine whether these gains reflect accurate assessment of skill reusability and identify which components of SkillSandbox contribute to them.