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LLM Distillation Research Paper
LLM Distillation Research Paper
This is the core paper behind the “Distilling Step-by-Step” idea I explain in the video. If you want the exact framing—why rationales matter, how the student is trained, and what results to expect—this is the reference I’d start with before coding.
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Pros
- +Clear motivation for why step-by-step supervision improves student reasoning
- +Gives a concrete framework you can translate into a training pipeline
- +Useful for designing evaluation beyond just final-answer accuracy
Cons
- -You still need engineering work to reproduce it cleanly on your own data
- -Results can be sensitive to how you generate and filter rationales
![How to Distill LLM? LLM Distilling [Explained] Step-by-Step using Python Hugging Face AutoTrain](https://img.youtube.com/vi/tpMOF1cT4Fc/mqdefault.jpg)