Autoregressive Direct Preference Optimization

• URL: http://arxiv.org/abs/2602.09533v1
• Date: Tue, 10 Feb 2026 08:45:30 GMT
• Title: Autoregressive Direct Preference Optimization
• Authors: Masanari Oi, Mahiro Ukai, Masahiro Kaneko, Naoaki Okazaki, Nakamasa Inoue,
• Abstract summary: We introduce a novel formulation that explicitly introduces the autoregressive assumption prior to applying the Bradley-Terry model.<n>We derive a novel variant, termed Autoregressive DPO (ADPO), that explicitly integrates autoregressive modeling into the preference optimization framework.<n>We show that there exist two length measures to be considered when designing DPO-based algorithms.
• Score: 42.22530165271681
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Direct preference optimization (DPO) has emerged as a promising approach for aligning large language models (LLMs) with human preferences. However, the widespread reliance on the response-level Bradley-Terry (BT) model may limit its full potential, as the reference and learnable models are assumed to be autoregressive only after deriving the objective function. Motivated by this limitation, we revisit the theoretical foundations of DPO and propose a novel formulation that explicitly introduces the autoregressive assumption prior to applying the BT model. By reformulating and extending DPO, we derive a novel variant, termed Autoregressive DPO (ADPO), that explicitly integrates autoregressive modeling into the preference optimization framework. Without violating the theoretical foundations, the derived loss takes an elegant form: it shifts the summation operation in the DPO objective outside the log-sigmoid function. Furthermore, through theoretical analysis of ADPO, we show that there exist two length measures to be considered when designing DPO-based algorithms: the token length $μ$ and the feedback length $μ$'. To the best of our knowledge, we are the first to explicitly distinguish these two measures and analyze their implications for preference optimization in LLMs.

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