TokenRatio: Principled Token-Level Preference Optimization via Ratio Matching

• URL: http://arxiv.org/abs/2605.12288v2
• Date: Thu, 14 May 2026 15:18:10 GMT
• Title: TokenRatio: Principled Token-Level Preference Optimization via Ratio Matching
• Abstract summary: We show how to recover token-level optimality using only standard sequence-level pairwise comparisons.<n>We introduce two instantiations: TBPO-Q, which explicitly learns a lightweight state baseline, and TBPO-A, which removes the baseline through advantage normalization.
• Score: 20.353416189523006
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Direct Preference Optimization (DPO) is a widely used RL-free method for aligning language models from pairwise preferences, but it models preferences over full sequences even though generation is driven by per-token decisions. Existing token-level extensions typically decompose a sequence-level Bradley-Terry objective across timesteps, leaving per-prefix (state-wise) optimality implicit. We study how to recover token-level preference optimality using only standard sequence-level pairwise comparisons. We introduce Token-level Bregman Preference Optimization (TBPO), which posits a token-level Bradley-Terry preference model over next-token actions conditioned on the prefix, and derive a Bregman-divergence density-ratio matching objective that generalizes the logistic/DPO loss while preserving the optimal policy induced by the token-level model and maintaining DPO-like simplicity. We introduce two instantiations: TBPO-Q, which explicitly learns a lightweight state baseline, and TBPO-A, which removes the baseline through advantage normalization. Across instruction following, helpfulness/harmlessness, and summarization benchmarks, TBPO improves alignment quality and training stability and increases output diversity relative to strong sequence-level and token-level baselines.

Related papers

• Mult-DPO: Multinomial Direct Preference Optimization for Recommender Systems [72.27018654934386]
  Direct preference optimization (DPO) is a simple and effective alignment strategy for large language models (LLMs) based on pairwise preferences.<n>In recommender systems, however, user feedback is rarely pairwise.<n>We propose Mult-DPO, a tractable multinomial surrogate likelihood over set-wise preference events for the user-preference alignment of LLM-based recommender systems.
  arXiv  Detail & Related papers  (2026-06-08T18:53:45Z)
• Balanced Aggregation: Understanding and Fixing Aggregation Bias in GRPO [70.38763678943648]
  Reinforcement learning with verifiable rewards (RLVR) has become a central paradigm for improving reasoning and code generation in large language models.<n>Standard GRPO uses sequence aggregation, while recent work has advocated token aggregation as a better alternative.<n>We show that these two rules induce different optimization biases; token aggregation introduces sign-length coupling, while sequence aggregation implicitly downweights longer responses.
  arXiv  Detail & Related papers  (2026-04-14T09:48:46Z)
• wDPO: Winsorized Direct Preference Optimization for Robust LLM Alignment [48.487557157323664]
  In practice, preference data are often noisy.<n>Existing robust variants of DPO mainly rely on uniform objective modifications or global reweighting.<n>We show that robust preference alignment benefits from addressing different noise types with targeted interventions.
  arXiv  Detail & Related papers  (2026-03-07T13:30:53Z)
• TAB-PO: Preference Optimization with a Token-Level Adaptive Barrier for Token-Critical Structured Generation [0.0]
  Token-weighted, reference-adjusted advantages that prioritize high-value semantic tokens are presented.<n>We evaluate TAB-PO on medical communication annotation.
  arXiv  Detail & Related papers  (2026-02-03T23:48:13Z)
• Principled RL for Diffusion LLMs Emerges from a Sequence-Level Perspective [85.06838178922791]
  Reinforcement Learning (RL) has proven highly effective for autoregressive language models.<n>But adapting these methods to diffusion large language models (dLLMs) presents fundamental challenges.<n>We propose a principled RL framework that treats entire sequence generation as a single action and uses the ELBO as a tractable sequence-level likelihood proxy.
  arXiv  Detail & Related papers  (2025-12-03T13:05:32Z)
• Not All Preferences are What You Need for Post-Training: Selective Alignment Strategy for Preference Optimization [0.0]
  Post-training alignment of large language models (LLMs) is a critical challenge, as not all tokens contribute equally to model performance.<n>This paper introduces a selective alignment strategy that prioritizes high-impact tokens within preference pairs.<n>By focusing on these informative tokens, our approach reduces computational overhead and enhances alignment fidelity.
  arXiv  Detail & Related papers  (2025-07-10T12:58:45Z)
• Multi-Preference Optimization: Generalizing DPO via Set-Level Contrasts [17.243429150450886]
  We propose $textbfMulti-Preference Optimization (MPO) to optimize over entire sets of responses.<n>MPO employs deviation-based weighting, which emphasizes outlier responses that deviate most from the mean reward.<n>We theoretically prove that MPO reduces alignment bias at a rate of $mathcalOleft(frac1sqrtnright)$ with respect to the number of responses per query.
  arXiv  Detail & Related papers  (2024-12-05T21:50:22Z)
• Bridging and Modeling Correlations in Pairwise Data for Direct Preference Optimization [75.1240295759264]
  We propose an effective framework for Bridging and Modeling Correlations in pairwise data, named BMC.<n>We increase the consistency and informativeness of the pairwise preference signals through targeted modifications.<n>We identify that DPO alone is insufficient to model these correlations and capture nuanced variations.
  arXiv  Detail & Related papers  (2024-08-14T11:29:47Z)
• Self-Augmented Preference Optimization: Off-Policy Paradigms for Language Model Alignment [104.18002641195442]
  We introduce Self-Augmented Preference Optimization (SAPO), an effective and scalable training paradigm that does not require existing paired data. Building on the self-play concept, which autonomously generates negative responses, we further incorporate an off-policy learning pipeline to enhance data exploration and exploitation.
  arXiv  Detail & Related papers  (2024-05-31T14:21:04Z)
• Token-level Direct Preference Optimization [8.249403373337024]
  Fine-tuning pre-trained Large Language Models is essential to align them with human values and intentions. We introduce Token-level Direct Preference Optimization (TDPO), a novel approach to align LLMs with human preferences by optimizing policy at the token level.
  arXiv  Detail & Related papers  (2024-04-18T08:49:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.