
Deep, research-driven explainers for AI engineers.
| Platform | Pricing | Freemium | Publishes | Twice weekly | |
|---|---|---|---|---|---|
| Issues | 234 | Founded | 3 years ago | Last Issue | 5 days ago |
| Active | |||||

LLM training is commonly done in three stages, and each stage helps the model to become more useful. These stages are as follows:
On-policy distillation (OPD) is a post-training method in which a student LLM generates its own responses, and a stronger teacher scores them token-by-token, with the student trained to minimize the reverse KL divergence between the two mod...
A simple and jargon-free guide to reading GPU specs for LLM training and inference.
GPU specification sheets are packed with jargon and confusing metrics. This guide simplifies them and helps you learn how to pick the best GPUs for your LL...
A simple and practical guide to GPU internals that AI engineers actually need.
“This is the lesson that you need if you are new to GPUs and want to master them without getting overwhelmed.”
✨ This newsletter features new research from Pathway. ✨
BDH-CQ is a 150M-parameter reasoning model that breaks the previously reported cost-accuracy Pareto frontier in ARC-AGI-1 and sets a new state-of-the-art for benchmark cost efficiency...
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