mlsanityData to 14 Oct 2026

Design Considerations in Offline Preference-based RL

arXiv 2502.06861 ↗First posted 8 Feb 2025cs.LG cs.AIcited by 4

On the map: LLM alignment / RLHF / preference optimisation → Reward modeling and RLHF

Abstract (arXiv)

Offline algorithms for Reinforcement Learning from Human Preferences (RLHF), which use only a fixed dataset of sampled responses given an input, and preference feedback among these responses, have gained increasing prominence in the literature on aligning language models. In this paper, we study how the different design choices made in methods such as DPO, IPO, SLiC and many variants influence the quality of the learned policy, from a theoretical perspective. Our treatment yields insights into the choices of loss function, the policy which is used to normalize log-likelihoods, and also the role of the data sampling policy. Notably, our results do not rely on the standard reparameterization-style arguments used to motivate some of the algorithms in this family, which allows us to give a unified treatment to a broad class of methods. We also conduct a small empirical study to verify some of the theoretical findings on a standard summarization benchmark.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Placed in Reward modeling and RLHF, one of 10 directions in LLM alignment / RLHF / preference optimisation. Builds on 27 of its 37 references that are in the map. First posted on arXiv 8 Feb 2025.

Builds on

27 of 37 references.

Closest ancestors · 9

Same direction, newest first

  1. 2024Scaling Laws for Reward Model Overoptimization in Direct Alignment Algorithms5 Jun
  2. 2024Value-Incentivized Preference Optimization: A Unified Approach to Online and Offline RLHF29 May
  3. 2024Provably Mitigating Overoptimization in RLHF: Your SFT Loss is Implicitly an Adversarial Regularizer26 May
  4. 2024A Minimaximalist Approach to Reinforcement Learning from Human Feedback8 Jan
  5. 2023Nash Learning from Human Feedback1 Dec
  6. 2023Is RLHF More Difficult than Standard RL?25 Jun
  7. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Model29 May
  8. 2023SLiC-HF: Sequence Likelihood Calibration with Human Feedback17 May
  9. 2017Deep reinforcement learning from human preferences12 Jun

Other references · 18

  1. 2022Training language models to follow instructions with human feedbackcited by 15,971
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  3. 2020Learning to summarize from human feedbackcited by 2,588
  4. 2024SimPO: Simple Preference Optimization with a Reference-Free Rewardcited by 963
  5. 2023A General Theoretical Paradigm to Understand Learning from Human Preferencescited by 912
  6. 2024Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translationcited by 394
  7. 2021Bellman-consistent Pessimism for Offline Reinforcement Learningcited by 269
  8. 2024Smaug: Fixing Failure Modes of Preference Optimisation with DPO-Positivecited by 241
  9. 2024Disentangling Length from Quality in Direct Preference Optimizationcited by 196
  10. 2024Generalized Preference Optimization: A Unified Approach to Offline Alignmentcited by 157
  11. 2024Direct Preference Optimization with an Offsetcited by 122
  12. 2022Calibrating Sequence likelihood Improves Conditional Language Generationcited by 121
6 more
  1. 2013Early stopping and non-parametric regression: An optimal data-dependent stopping rulecited by 89
  2. 2019Implicit Regularization for Optimal Sparse Recoverycited by 80
  3. 2024Correcting the Mythos of KL-Regularization: Direct Alignment without Overoptimization via Chi-Squared Preference Optimizationcited by 78
  4. 2024Robust Preference Optimization through Reward Model Distillationcited by 67
  5. 2018Iterate averaging as regularization for stochastic gradient descentcited by 27
  6. 2024On Regularization via Early Stopping for Least Squares Regressioncited by 11

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

  1. 2026Coverage Improvement and Fast Convergence of On-policy Preference Learningcited by 6
  2. 2025Beyond RLHF: A Unified Theoretical Framework of Alignmentcited by 3
  3. 2026From RLHF to Direct Alignment: A Theoretical Unification of Preference Learning for Large Language Modelscited by 0
  4. 2026Provably avoiding over-optimization in Direct Preference Optimization without knowing the data distributioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraint
  2. 2024The Importance of Online Data: Understanding Preference Fine-tuning via Coverage
  3. 2024Dataset Reset Policy Optimization for RLHF
  4. 2024Hybrid Preference Optimization for Alignment: Provably Faster Convergence Rates by Combining Offline Preferences with Online Exploration
  5. 2024WPO: Enhancing RLHF with Weighted Preference Optimization
  6. 2026Reinforcement Learning from Human Feedback: A Statistical Perspective
  7. 2023Benchmarks and Algorithms for Offline Preference-Based Reward Learning
  8. 2024Preference Elicitation for Offline Reinforcement Learning