mlsanityData to 7 Oct 2026

KL-Regularized RLHF with Multiple Reference Models: Exact Solutions and Sample Complexity

arXiv 2502.01203 ↗First posted 3 Feb 2025cs.LG stat.MLcited by 11

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

Abstract (arXiv)

Recent methods for aligning large language models (LLMs) with human feedback predominantly rely on a single reference model, which limits diversity, model overfitting, and underutilizes the wide range of available pre-trained models. Incorporating multiple reference models has the potential to address these limitations by broadening perspectives, reducing bias, and leveraging the strengths of diverse open-source LLMs. However, integrating multiple reference models into reinforcement learning with human feedback (RLHF) frameworks poses significant theoretical challenges, where achieving exact solutions has remained an open problem. This paper presents the first exact solution to the multiple reference model problem in reverse KL-regularized RLHF. We introduce a comprehensive theoretical framework that includes rigorous statistical analysis and provides sample complexity guarantees. Additionally, we extend our analysis to forward KL-regularized RLHF, offering new insights into sample complexity requirements in multiple reference scenarios. Our contributions lay the foundation for more advanced and adaptable LLM alignment techniques, enabling the effective use of multiple reference models. This work paves the way for developing alignment frameworks that are both theoretically sound and better suited to the challenges of modern AI ecosystems.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Placed in Reward modeling and RLHF, one of 10 directions in LLM alignment / RLHF / preference optimisation. Builds on 31 of its 44 references that are in the map. First posted on arXiv 3 Feb 2025.

Builds on

31 of 44 references.

Closest ancestors · 8

Same direction, newest first

  1. 2024Sharp Analysis for KL-Regularized Contextual Bandits and RLHF7 Nov
  2. 2024Skywork-Reward: Bag of Tricks for Reward Modeling in LLMs24 Oct
  3. 2024Inverse-RLignment: Large Language Model Alignment from Demonstrations through Inverse Reinforcement Learning24 May
  4. 2024Dataset Reset Policy Optimization for RLHF12 Apr
  5. 2023Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraint18 Dec
  6. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Model29 May
  7. 2019Fine-Tuning Language Models from Human Preferences18 Sep
  8. 2017Deep reinforcement learning from human preferences12 Jun

Other references · 23

  1. 2022Training language models to follow instructions with human feedbackcited by 15,712
  2. 2017Proximal Policy Optimization Algorithmscited by 14,441
  3. 2021Training Verifiers to Solve Math Word Problemscited by 9,743
  4. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 8,851
  5. 2024Qwen2.5 Technical Reportcited by 4,713
  6. 2023Mistral 7Bcited by 3,065
  7. 2022Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference timecited by 1,366
  8. 2020RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Modelscited by 1,235
  9. 2023The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Onlycited by 755
  10. 2023Beyond Reverse KL: Generalizing Direct Preference Optimization with Diverse Divergence Constraintscited by 174
  11. 2021Pessimistic Model-based Offline Reinforcement Learning under Partial Coveragecited by 160
  12. 2022Offline Reinforcement Learning with Realizability and Single-policy Concentrabilitycited by 122
11 more
  1. 2024Theoretical guarantees on the best-of-n alignment policycited by 116
  2. 2024Correcting the Mythos of KL-Regularization: Direct Alignment without Overoptimization via Chi-Squared Preference Optimizationcited by 78
  3. 2023Provable Offline Preference-Based Reinforcement Learningcited by 55
  4. 2024The Importance of Online Data: Understanding Preference Fine-tuning via Coveragecited by 55
  5. 2024InfAlign: Inference-aware language model alignmentcited by 34
  6. 2024Information Theoretic Guarantees For Policy Alignment In Large Language Modelscited by 22
  7. 2024Multi-Reference Preference Optimization for Large Language Modelscited by 10
  8. 2024Soft Preference Optimization: Aligning Language Models to Expert Distributionscited by 9
  9. 2022Learning to Constrain Policy Optimization with Virtual Trust Regioncited by 4
  10. 2024SALSA: Soup-based Alignment Learning for Stronger Adaptation in RLHFcited by 4
  11. 2016Data-driven nonlinear expectations for statistical uncertainty in decisionscited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 5; 2026: 6.

11 papers.

  1. 2025Reinforcement Learning with Verifiable Rewards: GRPO's Effective Loss, Dynamics, and Success Amplificationcited by 108
  2. 2025What Makes a Reward Model a Good Teacher? An Optimization Perspectivecited by 70
  3. 2026Demystifying Group Relative Policy Optimization: Its Policy Gradient is a U-Statisticcited by 19
  4. 2025SafeMobile: Chain-level Jailbreak Detection and Automated Evaluation for Multimodal Mobile Agentscited by 14
  5. 2025Robust Reinforcement Learning from Human Feedback for Large Language Models Fine-Tuningcited by 13
  6. 2026Provably Efficient Regularized Online RLHF with Generalized Bilinear Preferencescited by 5
  7. 2025Offline and Online KL-Regularized RLHF under Differential Privacycited by 3
  8. 2026Theoretical Perspectives on Data Quality and Synergistic Effects in Pre- and Post-Training Reasoning Modelscited by 3
  9. 2026Privacy-Preserving Reinforcement Learning from Human Feedback via Decoupled Reward Modelingcited by 3
  10. 2026f-Divergence Regularized RLHF: Two Tales of Sampling and Unified Analysescited by 2
  11. 2026Fast Rates for Offline Contextual Bandits with Forward-KL Regularization under Single-Policy Concentrabilitycited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2025Beyond RLHF: A Unified Theoretical Framework of Alignment
  2. 2024On The Global Convergence Of Online RLHF With Neural Parametrization
  3. 2024Sequence to Sequence Reward Modeling: Improving RLHF by Language Feedback
  4. 2025RLHF in an SFT Way: From Optimal Solution to Reward-Weighted Alignment
  5. 2025RLHF: A comprehensive Survey for Cultural, Multimodal and Low Latency Alignment Methods
  6. 2024SAIL: Self-Improving Efficient Online Alignment of Large Language Models
  7. 2024Does RLHF Scale? Exploring the Impacts From Data, Model, and Method
  8. 2024RLHF Deciphered: A Critical Analysis of Reinforcement Learning from Human Feedback for LLMs