Making Knowledge Distillation Cheap Enough to Run at Scale

Open-sourcing Knowledge Distillation Code and Weights of SD-Small and SD-Tiny

Model Distillation in the API

Fine-tune a cost-efficient model with the outputs of a large frontier model–all on the OpenAI platform

Characterize Then Distill: Mechanistic Reasoning in Large Output Spaces

arXiv:2606. 06840v1 Announce Type: cross Abstract: Modern reasoning models offer surprisingly strong zero-shot performance on challenging multi-label tasks that require selecting a small set of relevant options from hundreds of thousands to millions of candidate labels.

IDeaL: Data-Free Multi-Teacher Distillation via Improved Dead Leaves

The paper introduces IDeaL, a data‑free multi‑teacher distillation technique that generates teacher‑specific, improved samples using decorrelation losses at patch and image levels. By tailoring noise to each teacher, IDeaL produces strong student models that capture complementary teacher information and achieve results close to those distilled from real images. Experiments demonstrate that with only 1,000 images, students trained on IDeaL samples match or exceed the performance of students distilled from a 1,000‑image subset of ImageNet.

What Do Students Learn? A Feature-Level Analysis of Dark Knowledge

arXiv:2606. 03052v1 Announce Type: new Abstract: Knowledge Distillation (KD) is a powerful tool for model compression, yet the precise mechanisms by which student models acquire feature representations remain underexplored.

Be My Tutor: On-Policy Co-Distillation for Mutual LLM Improvement via Peer Feedback

arXiv:2606. 14368v1 Announce Type: new Abstract: We study multi-domain LLM training in which two models, each stronger in a different domain, co-evolve by tutoring each other through on-policy feedback.