Token-weighted Direct Preference Optimization with Attention
Direct Preference Optimization (DPO) aligns Large Language Models with human preferences without the need for a separate reward model. However, DPO treats all tokens in responses equally, neglecting the differing importance of individual tokens. Existing token-level PO methods compute the token weights using either token-position-based heuristic functions or probability estimates given by a separately trained model, which lacks robustness and incurs extra training cost. In contrast, we propose Token-weighted DPO (TwDPO) -- a novel training objective grounded on token-weighted RL -- and AttentionPO -- an instantiation of TwDPO that uses attention from the LLM itself to estimate token weigh...
| Authors | Chengyu Huang, Zhuohang Li, Sheng-Yen Chou et al. |
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| Primary category | cs.CL |
| Published | 21 May 2026 |
| Last updated at source | 25 August 2026 |
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