mlsanityData to 7 Oct 2026

Direct Preference Optimization with Rating Information: Practical Algorithms and Provable Gains

arXiv 2602.00603 ↗First posted 31 Jan 2026cs.LGcited by 2

On the map: LLM alignment / RLHF / preference optimisation → LLM preference optimization methods

Abstract (arXiv)

The class of direct preference optimization (DPO) algorithms has emerged as a promising approach for solving the alignment problem in foundation models. These algorithms work with very limited feedback in the form of pairwise preferences and fine-tune models to align with these preferences without explicitly learning a reward model. While the form of feedback used by these algorithms makes the data collection process easy and relatively more accurate, its ambiguity in terms of the quality of responses could have negative implications. For example, it is not clear if a decrease (increase) in the likelihood of preferred (dispreferred) responses during the execution of these algorithms could be interpreted as a positive or negative phenomenon. In this paper, we study how to design algorithms that can leverage additional information in the form of rating gap, which informs the learner how much the chosen response is better than the rejected one. We present new algorithms that can achieve faster statistical rates than DPO in presence of accurate rating gap information. Moreover, we theoretically prove and empirically show that the performance of our algorithms is robust to inaccuracy in rating gaps. Finally, we demonstrate the solid performance of our methods in comparison to a number of DPO-style algorithms across a wide range of LLMs and evaluation benchmarks.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in LLM preference optimization methods, one of 10 directions in LLM alignment / RLHF / preference optimisation. Builds on 44 of its 52 references that are in the map. First posted on arXiv 31 Jan 2026.

Builds on

44 of 52 references.

Closest ancestors · 18

Same direction, newest first

  1. 2025MaPPO: Maximum a Posteriori Preference Optimization with Prior Knowledge27 Jul
  2. 2025AMPO: Active Multi-Preference Optimization for Self-play Preference Selection25 Feb
  3. 2025Reward-aware Preference Optimization: A Unified Mathematical Framework for Model Alignment31 Jan
  4. 2024Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment19 Dec
  5. 2024Multi-Preference Optimization: Generalizing DPO via Set-Level Contrasts5 Dec
  6. 2024Unintentional Unalignment: Likelihood Displacement in Direct Preference Optimization11 Oct
  7. 2024Reward-Augmented Data Enhances Direct Preference Alignment of LLMs10 Oct
  8. 2024AIPO: Improving Training Objective for Iterative Preference Optimization13 Sep
  9. 2024Anchored Preference Optimization and Contrastive Revisions: Addressing Underspecification in Alignment12 Aug
  10. 2024Correcting the Mythos of KL-Regularization: Direct Alignment without Overoptimization via Chi-Squared Preference Optimization18 Jul
  11. 2024Robust Preference Optimization through Reward Model Distillation29 May
  12. 2024SimPO: Simple Preference Optimization with a Reference-Free Reward23 May
6 more
  1. 2024Is DPO Superior to PPO for LLM Alignment? A Comprehensive Study16 Apr
  2. 2024Smaug: Fixing Failure Modes of Preference Optimisation with DPO-Positive20 Feb
  3. 2024Noise Contrastive Alignment of Language Models with Explicit Rewards8 Feb
  4. 2023ULMA: Unified Language Model Alignment with Human Demonstration and Point-wise Preference5 Dec
  5. 2023A General Theoretical Paradigm to Understand Learning from Human Preferences18 Oct
  6. 2023Beyond Reverse KL: Generalizing Direct Preference Optimization with Diverse Divergence Constraints28 Sep

Other references · 26

  1. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  2. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 7,916
  3. 2017Deep reinforcement learning from human preferencescited by 3,846
  4. 2017Minimax Regret Bounds for Reinforcement Learningcited by 500
  5. 2024From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipelinecited by 484
  6. 2023Principled Reinforcement Learning with Human Feedback from Pairwise or K-wise Comparisonscited by 244
  7. 2024Iterative Reasoning Preference Optimizationcited by 244
  8. 2024Self-Play Preference Optimization for Language Model Alignmentcited by 228
  9. 2024Nemotron-4 340B Technical Reportcited by 143
  10. 2024Provably Mitigating Overoptimization in RLHF: Your SFT Loss is Implicitly an Adversarial Regularizercited by 109
  11. 2024On-Device Language Models: A Comprehensive Reviewcited by 101
  12. 2024Value-Incentivized Preference Optimization: A Unified Approach to Online and Offline RLHFcited by 73
14 more
  1. 2023Reinforcement Learning with Human Feedback: Learning Dynamic Choices via Pessimismcited by 69
  2. 2024REBEL: Reinforcement Learning via Regressing Relative Rewardscited by 66
  3. 2024Active Preference Optimization for Sample Efficient RLHFcited by 61
  4. 2023Provable Offline Preference-Based Reinforcement Learningcited by 55
  5. 2022Maximum-Likelihood Inverse Reinforcement Learning with Finite-Time Guaranteescited by 42
  6. 2024Dataset Reset Policy Optimization for RLHFcited by 41
  7. 2020Robust Inverse Reinforcement Learning under Transition Dynamics Mismatchcited by 33
  8. 2023Foundations of Reinforcement Learning and Interactive Decision Makingcited by 33
  9. 2024Regressing the Relative Future: Efficient Policy Optimization for Multi-turn RLHFcited by 29
  10. 2023Policy Gradient Algorithms for Robust MDPs with Non-Rectangular Uncertainty Setscited by 15
  11. 2022Robust Learning from Observation with Model Misspecificationcited by 9
  12. 2024Hybrid Reinforcement Learning from Offline Observation Alonecited by 5
  13. 2025Dual Formulation for Non-Rectangular Lp Robust Markov Decision Processescited by 3
  14. 2024Solving robust MDPs as a sequence of static RL problemscited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Provably avoiding over-optimization in Direct Preference Optimization without knowing the data distributioncited by 0
  2. 2026Disentangling Optimization Scale from Preference Scale in DPOcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Rethinking DPO: The Role of Rejected Responses in Preference Misalignment
  2. 2025Understanding the Impact of Sampling Quality in Direct Preference Optimization
  3. 2025Inducing Robustness in a 2 Dimensional Direct Preference Optimization Paradigm
  4. 2026Normalized Rewards for Preference Optimization
  5. 2025Proximalized Preference Optimization for Diverse Feedback Types: A Decomposed Perspective on DPO
  6. 2026AdaDPO: Self-Adaptive Direct Preference Optimization with Balanced Gradient Updates
  7. 2025What Matters in Data for DPO?
  8. 2024Towards Analyzing and Understanding the Limitations of DPO: A Theoretical Perspective