D-Skill framework distills agent skills without weight updates
Researchers from arXiv cs.AI introduced D-Skill, a framework designed to transfer capabilities from large frontier language-model agents to smaller models without updating the student's weights. Traditional knowledge distillation typically requires access to model parameters and additional training compute, but D-Skill treats the teacher model as a black box. It analyzes paired rollouts to…
Key points
- D-Skill distills teacher knowledge into natural-language skills without updating student model weights.
- The framework closes 75%-95% of the teacher-student performance gap on benchmark tasks.
- Student models using D-Skill incur 5.7x-8.3x lower inference costs than their teachers.
The method operates entirely offline from fixed trajectories, requiring no labels, further environment interaction, or weight updates after the initial rollout collection. The authors report that D-Skill closes 75%-95% of the performance gap between teacher and student models across SpreadsheetBench-Verified, ALFWorld, and chess puzzles. The approach outperforms one-epoch supervised fine-tuning baselines and remains competitive with multi-epoch fine-tuning.
A key advantage highlighted is the reduction in inference costs. When using models from the same family, the resulting student model incurs 5.7x-8.3x lower inference cost per task compared to the teacher. The study suggests that natural-language skill distillation offers a performant, interpretable, and efficient alternative to traditional weight-based capability transfer.
Coverage and discussion
2sources- SkillEval: Learning Interpretable Ability Profiles of LLMs via Cognitive Diagnosis ModelsPrimary source · arXiv cs.CL ·
The headline, key points and digest above were generated by Digest AI's editorial model from the linked sources. Automated summaries can contain errors: the sources are the record. Spotted a mistake? Tell us. Published by Martin K., who runs Digest AI and handles corrections.
More in Research
All →- PHP patches critical RCE flaws discovered with GPT-5.6 Sol, affecting up to 70% of websites · 9 src
- OpenAI and Google AI models discovered critical PHP flaw affecting millions of websites · 8 src
- Researchers release madartb, a parallel Multi-Dialect Arabic dependency treebank · 1 src
- Researchers release open DiffResearch framework with diffusion model backbone · 1 src
- Researchers find diffusion language models can pass Hidden messages between agents · 1 src
Comments
via GitHub Discussions