Scaling Optimal LR Across Token Horizons

• URL: http://arxiv.org/abs/2409.19913v2
• Date: Wed, 2 Oct 2024 17:03:25 GMT
• Title: Scaling Optimal LR Across Token Horizons
• Authors: Johan Bjorck, Alon Benhaim, Vishrav Chaudhary, Furu Wei, Xia Song,
• Abstract summary: We show how optimal learning rate depends on token horizon in LLM training. We also provide evidence that LLama-1 used too high LR, and estimate the performance hit from this.
• Score: 81.29631219839311
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: State-of-the-art LLMs are powered by scaling -- scaling model size, dataset size and cluster size. It is economically infeasible to extensively tune hyperparameter for the largest runs. Instead, approximately optimal hyperparameters must be inferred or \textit{transferred} from smaller experiments. Hyperparameter transfer across model sizes has been studied in Yang et al. However, hyperparameter transfer across dataset size -- or token horizon -- has not been studied yet. To remedy this we conduct a large scale empirical study on how optimal learning rate (LR) depends on token horizon in LLM training. We first demonstrate that the optimal LR changes significantly with token horizon -- longer training necessitates smaller LR. Secondly we demonstrate the the optimal LR follows a scaling law, and that the optimal LR for longer horizons can be accurately estimated from shorter horizons via such scaling laws. We also provide a rule-of-thumb for transferring LR across token horizons with zero overhead over current practices. Lastly we provide evidence that LLama-1 used too high LR, and estimate the performance hit from this. We thus argue that hyperparameter transfer across data size is an important and overlooked component of LLM training.

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