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A brief history of distillation in AI
Distillation has evolved from a compression tool into a central, contentious technique for transferring proprietary reasoning capabilities to open-source models.
What: Sergio Paniego details the history of distillation, noting how labs like DeepSeek, Qwen, and GLM now use the outputs of frontier models to train smaller models, complicating debates over intellectual property and copyright.
Why it matters: The legality and ethics of training 'student' models on 'teacher' model outputs remain a primary flashpoint between frontier labs and the open-source community.
Decoder
- Distillation: A training process where a smaller 'student' model is trained to mimic the outputs and behaviors of a larger, more complex 'teacher' model.
Original article
A brief history of distillation in AI
This is a warm-up for Class 2 of our Training an Agent series, where we distill open models with TRL. First, a short history. Distillation is a hot topic in post-training. The latest GLM-5.2 release...
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