Related papers: Training LLMs Beyond Next Token Prediction -- Filling the Mutual Information Gap

Training LLMs Beyond Next Token Prediction -- Filling the Mutual Information Gap

URL: http://arxiv.org/abs/2511.00198v1
Date: Fri, 31 Oct 2025 18:59:29 GMT
Title: Training LLMs Beyond Next Token Prediction -- Filling the Mutual Information Gap
Authors: Chun-Hao Yang, Bo-Han Feng, Tzu-Yuan Lai, Yan Yu Chen, Yin-Kai Dean Huang, Shou-De Lin,
Abstract summary: This work challenges the conventional approach of training large language models (LLMs) using next-token prediction (NTP)<n>We investigate the impact of the proposed solution in three kinds of tasks for LLMs: arithmetic, multi-label classification of text, and natural-language generation.
Score: 6.693221730277371
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Optimizing training performance in large language models (LLMs) remains an essential challenge, particularly in improving model performance while maintaining computational costs. This work challenges the conventional approach of training LLMs using next-token prediction (NTP), arguing that by predicting information-rich tokens during training, there is a more effective way to train LLMs. We investigate the impact of the proposed solution in three kinds of tasks for LLMs: arithmetic, multi-label classification of text, and natural-language generation. This work offers a principled approach to optimizing LLM training, advancing both model performance and theoretical understanding of the target-token selection strategies.

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