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arXiv AI By Jiazheng Li, Huaqing Zhang, Jing Xu, Tianle Cai, Jingzhao Zhang

Beyond Data Schedule: Fast LLM Pretraining on Data Mixtures by Loss-Reweighting

Read the original on arXiv AI →

The paper introduces LARE, a method that adjusts domain weights during large language model pretraining by reweighting losses based on running loss statistics, rather than altering data exposure. Experiments on a 1.5B model trained with 240B tokens show that LARE improves performance across five benchmarks, achieving significant gains on GSM8K (+17.06 pp) and MATH (+9.00 pp). The approach demonstrates that balancing loss scales can enhance joint capabilities without increasing domain exposure.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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