AI

D-Skill: Distilling Teacher Knowledge through Agent Skills

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Briefings

D-Skill is a new framework that distills teacher knowledge into natural-language skills by analyzing behavioral divergences in paired agent rollouts, rather than updating model weights. This approach achieves 75%-95% of the teacher-student gap on benchmark tasks like ALFWorld and chess puzzles while reducing inference costs by 5.7x to 8.3x compared to the teacher model. The method operates entirely offline, requiring no training compute, label access, or post-rollout weight updates.

Key points

• D-Skill distills behavioral advantages into interpretable natural-language skills by analyzing paired rollouts and identifying divergences, avoiding weight updates.
• The framework achieves 75%-95% of the teacher-student gap on benchmark tasks like ALFWorld and chess puzzles while reducing inference costs by 5.7x to 8.3x.

Context

This research addresses the challenge of transferring high-performance language-model agent capabilities to smaller models, which are essential for efficiency. D-Skill offers an alternative to traditional parameter-based distillation by focusing on behavioral analysis instead of weight updates.

Limitations

Results are limited to SpreadsheetBench-Verified, ALFWorld, and chess puzzles across multiple teacher-student pairs. The study does not report on long-term performance stability, domain generalization, or the specific dataset composition used for the benchmark tasks.

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