Rho-1: Not All Tokens Are What You Need
Authors: name: Zhenghao Lin, name: Zhibin Gou, name: Yeyun Gong, name: Xiao Liu, name: Yelong Shen, name: Ruochen Xu, name: Chen Lin, name: Yujiu Yang, name: Jian Jiao, name: Nan Duan, name: Weizhu Chen

Previous language model pre-training methods have uniformly applied a
next-token prediction loss to all training tokens. Challenging this norm, we
posit that "Not all tokens in a corpus are equally important for language model
training". Our initial analysis delves into token-level training dynamics of
language model, revealing distinct loss patterns for different tokens.
Leveraging these insights, we introduce a new language model called Rho-1.
Unlike traditional LMs that learn to predict every next token in a corpus,
Rho-1 employs Selective Language Modeling (SLM), which selectively trains on
useful tokens that aligned with the desired distribution. This approach
involves scoring pretraining tokens using a reference model, and then training
the language model with a focused loss on tokens with higher excess loss. When
continual pretraining on 15B OpenWebMath corpus, Rho-1 yields an absolute
improvement in few-shot accuracy of up to 30% in 9 math tasks. After
fine-tuning, Rho-1-1B and 7B achieved state-of-the-art results of 40.6% and
51.8% on MATH dataset, respectively - matching DeepSeekMath with only 3% of the
pretraining tokens. Furthermore, when pretraining on 80B general tokens, Rho-1
achieves 6.8% average enhancement across 15 diverse tasks, increasing both
efficiency and performance of the language model pre-training.