Se-DPO: Self-Evolving Token Credit for Direct Preference Optimization
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Computer Science > Computation and Language
arXiv:2608.09568 (cs)
[Submitted on 10 Aug 2026]
Title:Se-DPO: Self-Evolving Token Credit for Direct Preference Optimization
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Abstract:Direct Preference Optimization (DPO) aggregates token-level log-probability ratios via uniform summation, implicitly treating all tokens as contributing equally to the preference signal. However, the contribution of individual tokens to the preference signal varies. We introduce token credit, which modulates each token's KL regularization based on its contribution to the preference outcome. We derive that effective token credit is proportional to the magnitude of each token's implicit reward, and observe that this quantity evolves substantially during training. This implies that static token credit becomes increasingly misaligned as training progresses. In this work, we propose Se-DPO (Self-Evolving Token Credit for DPO), a live mechanism that derives token credit from the model's own evolving internal signals during DPO training. Since the reward signal varies in reliability across positions, Se-DPO calibrates token credit based on both the strength and the confidence of each token's contribution. Se-DPO requires no external models, adding only a lightweight calibration network with minimal computational overhead. Experiments show that Se-DPO improves over DPO by up to 9.8 points on AlpacaEval~2 and 12.2 points on Arena-Hard.
| Comments: | 16 pages, 2 figures, COLM2026 |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2608.09568 [cs.CL] |
| (or arXiv:2608.09568v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2608.09568
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled Se-DPO: Self-Evolving Token Credit for Direct Preference Optimization, by Wenxiao Zhao and 2 other authors
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