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Artifact Distillation: Training-Free Transfer from Frontier to Small Language Models
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First announced by arXiv: 2026-10-30 · Indexed by Pub-AI: 2026-10-30 UTC
Artifact Distillation: Training-Free Transfer from Frontier to Small Language Models
First announced by arXiv: 2026-10-30 · Original record ↗
- arxiv
- 2610.37680
Explicit source versions (1)
- v1 · Date not supplied
- Preprint available
Source date: 2026-10-30 · First detected: 2026-10-30 UTC · Historical baseline
Source evidence (1)
- Official evidence ↗ · RSS guid=oai:arXiv.org:2610.37680v1; arxiv:announce_type=new; item pubDate (official metadata)
Abstract · arxiv
Closing the gap between frontier and smaller language models is usually approached by distillation: fine-tuning the small model (student) on a frontier model's (teacher) outputs, which binds transfer to training compute and leaves the transferred knowledge largely opaque. We propose Artifact Distillation: a teacher solves training instances and externalizes its problem-solving procedure into an artifact bank that a student queries at inference time, with no gradient updates. Across four LiveBench tasks, a bank written by Claude Opus 4.5 lets Claude Haiku 4.5 outperform unaided Claude Sonnet 4.5 on spatial reasoning (0.707 vs. 0.647) at 60% of its cost, and reach Sonnet 4.5's level on table joining at 89% of its cost. The same raw bank raises Qwen3.5-27B's pooled score from 0.730 to 0.800, showing that transfer is not confined to the teacher's model family.