@ben_burtenshaw: before model distillation was an attack vector. it was. pretty handy way of improving model performance on a task you c…
Summary
Ben Burtenshaw announces a live stream on July 7th covering knowledge distillation in post-training, showing how to implement it using small models to approach large model performance.
View Cached Full Text
Cached at: 06/30/26, 03:48 PM
before model distillation was an attack vector. it was. pretty handy way of improving model performance on a task you care about.
especially if you want to take small, local, or cheap model and improve it on a tasks typically reserved to large models.
in the next live stream, we’re going to break down knowledge distillation in post-training and show you how to implement it.
going out next week: July 7th 8am PST, 5pm CEST live on: @huggingface X, YT, LI
Similar Articles
@SergioPaniego: before model distillation was an attack vector. it was. pretty handy way of improving model performance on a task you c…
The article provides a brief history of model distillation in AI and announces an upcoming live stream class on distilling open models using TRL (Transformer Reinforcement Learning).
@huggingface: Training Agents 2: Live tutorial on model distillation for training custom agents.
Hugging Face hosted a live tutorial on model distillation for training custom agents, available as a replay.
KDFP: A first-principles approach to knowledge distillation in large language models
KDFP: A first-principles approach to knowledge distillation in large language models Source: https://arxiv.org/html/2610.10854 Ryan Swift & Konstantinos PsounisAffiliation:Thomas Lord Department of Computer ScienceAffiliation:University of Southern CaliforniaAffiliation:Los Angeles, CA 90007, USAEmail:[{ryanswif,kpsounis}@usc.edu](mailto:) ###### Abstract Knowledge distillation is an established technique for improving the capabilities of small, efficient student models by training them with the representations of larger, more capable teacher models. Much of the recent work in the distillation of large language models (LLMs) has focused on distilling abilities learned during post-training, such as instruction following, chain-of-thought reasoning, and tool usage. This has left a large research gap in general knowledge distillation for LLMs, which is essential for developing efficient and private systems suitable for deployment on edge devices. We take a first-principles approach, evaluating previous lessons from prior works and conducting new explorations to develop a distillation methodology suitable for modern LLMs. We present KDFP, a novel methodology for white-box general knowledge distillation in LLMs. We demonstrate that KDFP outperforms existing methods by 1.6% – 4.9% across 9 benchmarks while increasing training efficiency by up to 99.1% through ephemeral parameter reduction.
@zhaisf: These were some magical results from distillation by @geoffreyhinton that really shocked me when I first saw them, and …
The article discusses surprising robustness of model distillation with respect to training distribution, even with little overlap with target distribution, and its implications for on/off-policy distillation.
@TheTuringPost: https://x.com/TheTuringPost/status/2068474648925216861
An educational overview of knowledge distillation, covering its history, core concepts like softmax and temperature, types, scaling laws, and practical examples including DeepSeek-R1.