Fine-tuning pre-trained Large Language Models (LLMs) is essential to align them with human values and intentions. This process often uti- lizes 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 pa- per, we introduce Token-level Direct Preference Optimization (TDPO), a novel approach to align LLMs with human preferences by optimizing pol- icy at the token level. Unlike previous methods, which face challenges in divergence efficiency, TDPO incorporates forward KL divergence con- straints 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 preserv- ing simplicity without the need for explicit re- ward modeling. Experimental results across vari- ous text tasks demonstrate TDPO’s superior per- formance in balancing alignment with genera- tion 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.