mlsanityData to 7 Oct 2026

Towards a Theoretical Understanding to the Generalization of RLHF

arXiv 2601.16403 ↗First posted 23 Jan 2026cs.LGcited by 5

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

Abstract (arXiv)

Reinforcement Learning from Human Feedback (RLHF) and its variants have emerged as the dominant approaches for aligning Large Language Models with human intent. While empirically effective, the theoretical generalization properties of these methods in high-dimensional settings remain to be explored. To this end, we build the generalization theory on RLHF of LLMs under the linear reward model, through the framework of algorithmic stability. In contrast to the existing works built upon the consistency of maximum likelihood estimations on reward model, our analysis is presented under an end-to-end learning framework, which is consistent with practice. Concretely, we prove that under a key feature coverage condition, the empirical optima of policy model have a generalization bound of order O(n^-frac12). Moreover, the results can be extrapolated to parameters obtained by gradient-based learning algorithms, i.e., Gradient Ascent (GA) and Stochastic Gradient Ascent (SGA). Thus, we argue that our results provide new theoretical evidence for the empirically observed generalization of LLMs after RLHF.

Key facts. Cited by 5 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 44 of its 53 references that are in the map. First posted on arXiv 23 Jan 2026.

Builds on

44 of 53 references.

Closest ancestors · 8

Same direction, newest first

  1. 2024Sharp Analysis for KL-Regularized Contextual Bandits and RLHF7 Nov
  2. 2024RLHF Deciphered: A Critical Analysis of Reinforcement Learning from Human Feedback for LLMs12 Apr
  3. 2024Dataset Reset Policy Optimization for RLHF12 Apr
  4. 2023Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraint18 Dec
  5. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Model29 May
  6. 2023Principled Reinforcement Learning with Human Feedback from Pairwise or K-wise Comparisons26 Jan
  7. 2019Fine-Tuning Language Models from Human Preferences18 Sep
  8. 2017Deep reinforcement learning from human preferences12 Jun

Other references · 36

  1. 2022Training language models to follow instructions with human feedbackcited by 15,712
  2. 2025Qwen3 Technical Reportcited by 8,195
  3. 2025Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilitiescited by 3,655
  4. 2023A General Theoretical Paradigm to Understand Learning from Human Preferencescited by 906
  5. 2019How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddingscited by 770
  6. 2013Stochastic First- and Zeroth-order Methods for Nonconvex Stochastic Programmingcited by 716
  7. 2020Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuningcited by 658
  8. 2014Convex Optimization: Algorithms and Complexitycited by 569
  9. 2015Train faster, generalize better: Stability of stochastic gradient descentcited by 547
  10. 2016Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Lojasiewicz Conditioncited by 526
  11. 2025VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Modelcited by 515
  12. 2019Provably Efficient Reinforcement Learning with Linear Function Approximationcited by 514
24 more
  1. 2024Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Modelscited by 467
  2. 2025π*0.6: a VLA That Learns From Experiencecited by 280
  3. 2024Is DPO Superior to PPO for LLM Alignment? A Comprehensive Studycited by 262
  4. 2019A Theoretical Analysis of Deep Q-Learningcited by 212
  5. 2020Stability of Stochastic Gradient Descent on Nonsmooth Convex Lossescited by 172
  6. 2020Fine-Grained Analysis of Stability and Generalization for Stochastic Gradient Descentcited by 118
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  11. 2018Tight Analyses for Non-Smooth Stochastic Gradient Descentcited by 94
  12. 2019Sharper bounds for uniformly stable algorithmscited by 91
  13. 2020PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learningcited by 91
  14. 2017Data-Dependent Stability of Stochastic Gradient Descentcited by 90
  15. 2022Human-in-the-loop: Provably Efficient Preference-based Reinforcement Learning with General Function Approximationcited by 80
  16. 2020Preference-based Reinforcement Learning with Finite-Time Guaranteescited by 65
  17. 2023Provable Offline Preference-Based Reinforcement Learningcited by 55
  18. 2024The Importance of Online Data: Understanding Preference Fine-tuning via Coveragecited by 55
  19. 2025VLA-R1: Enhancing Reasoning in Vision-Language-Action Modelscited by 38
  20. 2025Online Preference Alignment for Language Models via Count-based Explorationcited by 24
  21. 2017Empirical Risk Minimization for Stochastic Convex Optimization: O(1/n)- and O(1/n2)-type of Risk Boundscited by 15
  22. 2022Generalization Error Bounds for Deep Neural Networks Trained by SGDcited by 14
  23. 2020Characterization of Excess Risk for Locally Strongly Convex Population Riskcited by 5
  24. 2024Stability and Sharper Risk Bounds with Convergence Rate tildeO(1/n2)cited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 5.

5 papers.

  1. 2026Maximum Likelihood Reinforcement Learningcited by 29
  2. 2026Reasoning and Tool-use Compete in Agentic RL:From Quantifying Interference to Disentangled Tuningcited by 15
  3. 2026ETS: Energy-Guided Test-Time Scaling for Training-Free RL Alignmentcited by 3
  4. 2026Generalisation of RLHF under Reward Shift and Clipped KL Regularisationcited by 0
  5. 2026Turning Off-Policy Tokens On-Policy: A Plug-in Approach for Improving LLM Alignmentcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Reinforcement Learning from Human Feedback: A Statistical Perspective
  2. 2023Understanding the Effects of RLHF on LLM Generalisation and Diversity
  3. 2025Reward Model Overoptimisation in Iterated RLHF
  4. 2024Zeroth-Order Policy Gradient for Reinforcement Learning from Human Feedback without Reward Inference
  5. 2024Reward-Robust RLHF in LLMs
  6. 2024Does RLHF Scale? Exploring the Impacts From Data, Model, and Method
  7. 2024Exploration-Driven Policy Optimization in RLHF: Theoretical Insights on Efficient Data Utilization
  8. 2024Personalizing Reinforcement Learning from Human Feedback with Variational Preference Learning