  Fine-tuning pre-trained Large Language Models (LLMs) is essential to align
them with human values and intentions. This process often utilizes methods like
pairwise comparisons and KL divergence against a reference LLM, focusing on the
evaluation of full answers generated by the models. However, the generation of
these responses occurs in a token level, following a sequential,
auto-regressive fashion. In this paper, we introduce Token-level Direct
Preference Optimization (TDPO), a novel approach to align LLMs with human
preferences by optimizing policy at the token level. Unlike previous methods,
which face challenges in divergence efficiency, TDPO incorporates forward KL
divergence constraints for each token, improving alignment and diversity.
Utilizing the Bradley-Terry model for a token-based reward system, TDPO
enhances the regulation of KL divergence, while preserving simplicity without
the need for explicit reward modeling. Experimental results across various text
tasks demonstrate TDPO's superior performance in balancing alignment with
generation diversity. Notably, fine-tuning with TDPO strikes a better balance
than DPO in the controlled sentiment generation and single-turn dialogue
datasets, and significantly improves the quality of generated responses
compared to both DPO and PPO-based RLHF methods. Our code is open-sourced at
https://github.com/Vance0124/Token-level-Direct-Preference-Optimization.
