mlsanityData to 7 Oct 2026

PARL: A Unified Framework for Policy Alignment in Reinforcement Learning from Human Feedback

arXiv 2308.02585 ↗First posted 3 Aug 2023cs.LGcited by 38

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

Abstract (arXiv)

We present a novel unified bilevel optimization-based framework, textsfPARL, formulated to address the recently highlighted critical issue of policy alignment in reinforcement learning using utility or preference-based feedback. We identify a major gap within current algorithmic designs for solving policy alignment due to a lack of precise characterization of the dependence of the alignment objective on the data generated by policy trajectories. This shortfall contributes to the sub-optimal performance observed in contemporary algorithms. Our framework addressed these concerns by explicitly parameterizing the distribution of the upper alignment objective (reward design) by the lower optimal variable (optimal policy for the designed reward). Interestingly, from an optimization perspective, our formulation leads to a new class of stochastic bilevel problems where the stochasticity at the upper objective depends upon the lower-level variable. True to our best knowledge, this work presents the first formulation of the RLHF as a bilevel optimization problem which generalizes the existing RLHF formulations and addresses the existing distribution shift issues in RLHF formulations. To demonstrate the efficacy of our formulation in resolving alignment issues in RL, we devised an algorithm named textsfA-PARL to solve PARL problem, establishing sample complexity bounds of order O(1/T). Our empirical results substantiate that the proposed textsfPARL can address the alignment concerns in RL by showing significant improvements (up to 63% in terms of required samples) for policy alignment in large-scale environments of the Deepmind control suite and Meta world tasks.

Key facts. Cited by 38 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 60 of its 98 references that are in the map. First posted on arXiv 3 Aug 2023.

Builds on

60 of 98 references.

Closest ancestors · 8

Same direction, newest first

  1. 2023Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback27 Jul
  2. 2023Principled Reinforcement Learning with Human Feedback from Pairwise or K-wise Comparisons26 Jan
  3. 2022Rewards Encoding Environment Dynamics Improves Preference-based Reinforcement Learning12 Nov
  4. 2022Scaling Laws for Reward Model Overoptimization19 Oct
  5. 2022SURF: Semi-supervised Reward Learning with Data Augmentation for Feedback-efficient Preference-based Reinforcement Learning18 Mar
  6. 2021PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training9 Jun
  7. 2018Reward learning from human preferences and demonstrations in Atari15 Nov
  8. 2017Deep reinforcement learning from human preferences12 Jun

Other references · 52

  1. 2015Continuous control with deep reinforcement learningcited by 3,279
  2. 2021Convex optimizationcited by 2,479
  3. 2016Generative Adversarial Imitation Learningcited by 1,576
  4. 2019Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learningcited by 1,168
  5. 2018DeepMind Control Suitecited by 935
  6. 2018A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progresscited by 294
  7. 2020On the Global Convergence Rates of Softmax Policy Gradient Methodscited by 258
  8. 2019Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observationscited by 256
  9. 2022The Alignment Problem from a Deep Learning Perspectivecited by 246
  10. 2018Approximation Methods for Bilevel Programmingcited by 231
  11. 2020Bilevel Optimization: Convergence Analysis and Enhanced Designcited by 219
  12. 2019A Divergence Minimization Perspective on Imitation Learning Methodscited by 188
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  2. 2015Maximum Entropy Deep Inverse Reinforcement Learningcited by 173
  3. 2016A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Modelscited by 167
  4. 2023Fundamental Limitations of Alignment in Large Language Modelscited by 152
  5. 2019Global Convergence of Policy Gradient Methods to (Almost) Locally Optimal Policiescited by 117
  6. 2018Deep Hedgingcited by 106
  7. 2021A Near-Optimal Algorithm for Stochastic Bilevel Optimization via Double-Momentumcited by 106
  8. 2021Provably Faster Algorithms for Bilevel Optimizationcited by 106
  9. 2021Dueling RL: Reinforcement Learning with Trajectory Preferencescited by 105
  10. 2015Contextual Dueling Banditscited by 101
  11. 2018Preference-based Online Learning with Dueling Bandits: A Surveycited by 100
  12. 2023A Fully First-Order Method for Stochastic Bilevel Optimizationcited by 97
  13. 2021A Single-Timescale Method for Stochastic Bilevel Optimizationcited by 69
  14. 2020Preference-based Reinforcement Learning with Finite-Time Guaranteescited by 65
  15. 2021A Fully Single Loop Algorithm for Bilevel Optimization without Hessian Inversecited by 53
  16. 2016Model-Free Imitation Learning with Policy Optimizationcited by 52
  17. 2020On Linear Convergence of Policy Gradient Methods for Finite MDPscited by 51
  18. 2021Lower Bounds and Accelerated Algorithms for Bilevel Optimizationcited by 46
  19. 2021Sample-Efficient Learning of Stackelberg Equilibria in General-Sum Gamescited by 40
  20. 2019Convergence of Learning Dynamics in Stackelberg Gamescited by 28
  21. 2021Enhanced Bilevel Optimization via Bregman Distancecited by 28
  22. 2023Averaged Method of Multipliers for Bi-Level Optimization without Lower-Level Strong Convexitycited by 26
  23. 2021Can Reinforcement Learning Find Stackelberg-Nash Equilibria in General-Sum Markov Games with Myopic Followers?cited by 24
  24. 2022Local Stochastic Bilevel Optimization with Momentum-Based Variance Reductioncited by 20
  25. 2015Watch and Learn: Optimizing from Revealed Preferences Feedbackcited by 17
  26. 2023On Momentum-Based Gradient Methods for Bilevel Optimization with Nonconvex Lower-Levelcited by 14
  27. 2013Credal Networks under Maximum Entropycited by 13
  28. 2022On the Hidden Biases of Policy Mirror Ascent in Continuous Action Spacescited by 11
  29. 2021Solving Heterogeneous General Equilibrium Economic Models with Deep Reinforcement Learningcited by 9
  30. 2014Learning Economic Parameters from Revealed Preferencescited by 8
  31. 2021Projection-Free Algorithm for Stochastic Bi-level Optimizationcited by 8
  32. 2018Inference Aided Reinforcement Learning for Incentive Mechanism Design in Crowdsourcingcited by 7
  33. 2022Posterior Coreset Construction with Kernelized Stein Discrepancy for Model-Based Reinforcement Learningcited by 7
  34. 2019Parenting: Safe Reinforcement Learning from Human Inputcited by 6
  35. 2022Learning Dynamic Mechanisms in Unknown Environments: A Reinforcement Learning Approachcited by 5
  36. 2022Pessimism meets VCG: Learning Dynamic Mechanism Design via Offline Reinforcement Learningcited by 4
  37. 2019Simple Algorithms for Dueling Banditscited by 3
  38. 2022Dealing with Sparse Rewards in Continuous Control Robotics via Heavy-Tailed Policiescited by 2

50 most cited of 52 All 52 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 13; 2025: 11; 2026: 12.

38 papers.

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  1. 2024Transfer Q Star: Principled Decoding for LLM Alignmentcited by 46
  2. 2024Principled Penalty-based Methods for Bilevel Reinforcement Learning and RLHFcited by 41
  3. 2024Adaptive Preference Scaling for Reinforcement Learning with Human Feedbackcited by 20
  4. 2025A Technical Survey of Reinforcement Learning Techniques for Large Language Modelscited by 19
  5. 2024Self-Play with Adversarial Critic: Provable and Scalable Offline Alignment for Language Modelscited by 16
  6. 2025Game Theory Meets Large Language Models: A Systematic Survey with Taxonomy and New Frontierscited by 14
  7. 2025Collab: Controlled Decoding using Mixture of Agents for LLM Alignmentcited by 13
  8. 2024Zeroth-Order Policy Gradient for Reinforcement Learning from Human Feedback without Reward Inferencecited by 12
  9. 2024Bilevel reinforcement learning via the development of hyper-gradient without lower-level convexitycited by 10
  10. 2025On The Sample Complexity Bounds In Bilevel Reinforcement Learningcited by 10
  11. 2024Varying Shades of Wrong: Aligning LLMs with Wrong Answers Onlycited by 9
  12. 2024Principal-Agent Reinforcement Learning: Orchestrating AI Agents with Contractscited by 8
26 more
  1. 2025VARP: Reinforcement Learning from Vision-Language Model Feedback with Agent Regularized Preferencescited by 8
  2. 2024LancBiO: dynamic Lanczos-aided bilevel optimization via Krylov subspacecited by 4
  3. 2025Sign-SZPO: Provable Preference-based Reinforcement Learning with an Unknown Link Functioncited by 4
  4. 2024On The Global Convergence Of Online RLHF With Neural Parametrizationcited by 3
  5. 2024Learning from Response not Preference: A Stackelberg Approach for LLM Detoxification using Non-parallel Datacited by 3
  6. 2025Bi-Level Policy Optimization with Nystr"om Hypergradientscited by 3
  7. 2026Explaining and Preventing Alignment Collapse in Iterative RLHFcited by 3
  8. 2024Learning to Steer Markovian Agents under Model Uncertaintycited by 2
  9. 2025Multi-Objective Bilevel Learningcited by 2
  10. 2026Reward Shaping for (Inference-Time) Alignment: A Stackelberg Game Perspectivecited by 2
  11. 2026Sample-Efficient Hypergradient Estimation for Decentralized Bi-Level Reinforcement Learningcited by 2
  12. 2024Robust Reward Design for Markov Decision Processescited by 1
  13. 2026Oracle-Robust Online Alignment for Large Language Modelscited by 1
  14. 2026A Tale of Two Problems: Multi-Task Bilevel Learning Meets Equality Constrained Multi-Objective Optimizationcited by 1
  15. 2026On the Convergence of Self-Improving Online LLM Alignmentcited by 1
  16. 2023Sample Complexity of Preference-Based Nonparametric Off-Policy Evaluation with Deep Networkscited by 0
  17. 2023REBEL: Reward Regularization-Based Approach for Robotic Reinforcement Learning from Human Feedbackcited by 0
  18. 2025HERAKLES: Hierarchical Skill Compilation for Open-ended LLM Agentscited by 0
  19. 2025Rethinking How AI Embeds and Adapts to Human Values: Challenges and Opportunitiescited by 0
  20. 2025From Data to Rewards: a Bilevel Optimization Perspective on Maximum Likelihood Estimationcited by 0
  21. 2026Sample Complexity Analysis for Constrained Bilevel Reinforcement Learningcited by 0
  22. 2026Deep Incentive Design with Differentiable Equilibrium Blockscited by 0
  23. 2026Offline Constrained RLHF with Multiple Preference Oraclescited by 0
  24. 2026Hypergradient-based Bilevel Reinforcement Learning with Improved Sample Complexitycited by 0
  25. 2026Efficient Hypergradient Descent for Inverse Reinforcement Learningcited by 0
  26. 2026Efficient Hessian-Free Methods for Multi-Objective Bilevel Optimization with Nonconvex Lower Levelcited by 0

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  2. 2023Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraint
  3. 2025Reinforcement Learning from Human Feedback
  4. 2023Query-Policy Misalignment in Preference-Based Reinforcement Learning
  5. 2026Reinforcement Learning from Human Feedback: A Statistical Perspective
  6. 2025One Framework to Rule Them All: Unifying RL-Based and RL-Free Methods in RLHF
  7. 2024COMAL: A Convergent Meta-Algorithm for Aligning LLMs with General Preferences
  8. 2024Exploration-Driven Policy Optimization in RLHF: Theoretical Insights on Efficient Data Utilization