A Token is Worth over 1,000 Tokens: Efficient Knowledge Distillation through Low-Rank Clone — mentioned by namr2000

Tue, 16 Jun 2026 18:48:48 GMT

You don't have to train from scratch but you can. Distillation ends up being somewhere in the ballpark of 1000x faster to train [1]. It also comes with the huge advantage of not needing to create RLHF datasets, since you can just copy the behavior of the teacher model. This saves an enormous amount of labeling money at the cost of making the model behave similarly to the teacher. If you are training from scratch, you can look at LLM scaling laws to figure out roughly the compute budget you need to optimally train a model [2]. Based on [2] a 30B model needs something like 2e+23 FLOPS to train…

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A Token is Worth over 1,000 Tokens: Efficient Knowledge Distillation through Low-Rank Clone · HackerPapers discussions

A Token is Worth over 1,000 Tokens: Efficient Knowledge Distillation through Low-Rank Clone — mentioned by namr2000