mlsanityData to 14 Oct 2026

Hindsight PRIORs for Reward Learning from Human Preferences

arXiv 2404.08828 ↗First posted 12 Apr 2024cs.LG cs.AI cs.HCcited by 12

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

Abstract (arXiv)

Preference based Reinforcement Learning (PbRL) removes the need to hand specify a reward function by learning a reward from preference feedback over policy behaviors. Current approaches to PbRL do not address the credit assignment problem inherent in determining which parts of a behavior most contributed to a preference, which result in data intensive approaches and subpar reward functions. We address such limitations by introducing a credit assignment strategy (Hindsight PRIOR) that uses a world model to approximate state importance within a trajectory and then guides rewards to be proportional to state importance through an auxiliary predicted return redistribution objective. Incorporating state importance into reward learning improves the speed of policy learning, overall policy performance, and reward recovery on both locomotion and manipulation tasks. For example, Hindsight PRIOR recovers on average significantly (p<0.05) more reward on MetaWorld (20%) and DMC (15%). The performance gains and our ablations demonstrate the benefits even a simple credit assignment strategy can have on reward learning and that state importance in forward dynamics prediction is a strong proxy for a state's contribution to a preference decision. Code repository can be found at https://github.com/apple/ml-rlhf-hindsight-prior.

Key facts. Cited by 12 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 42 of its 59 references that are in the map. First posted on arXiv 12 Apr 2024.

Builds on

42 of 59 references.

Closest ancestors · 8

Same direction, newest first

  1. 2023Principled Reinforcement Learning with Human Feedback from Pairwise or K-wise Comparisons26 Jan
  2. 2022Reward Uncertainty for Exploration in Preference-based Reinforcement Learning24 May
  3. 2022SURF: Semi-supervised Reward Learning with Data Augmentation for Feedback-efficient Preference-based Reinforcement Learning18 Mar
  4. 2021PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training9 Jun
  5. 2019Fine-Tuning Language Models from Human Preferences18 Sep
  6. 2018Scalable agent alignment via reward modeling: a research direction19 Nov
  7. 2018Reward learning from human preferences and demonstrations in Atari15 Nov
  8. 2017Deep reinforcement learning from human preferences12 Jun

Other references · 34

  1. 2022Training language models to follow instructions with human feedbackcited by 15,971
  2. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,733
  3. 2013Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Mapscited by 2,371
  4. 2019Dream to Control: Learning Behaviors by Latent Imaginationcited by 1,653
  5. 2018Learning Latent Dynamics for Planning from Pixelscited by 1,264
  6. 2018Soft Actor-Critic Algorithms and Applicationscited by 1,240
  7. 2019Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learningcited by 1,204
  8. 2019Attention is not not Explanationcited by 636
  9. 2023Aligning Text-to-Image Models using Human Feedbackcited by 422
  10. 2020dm_control: Software and Tasks for Continuous Controlcited by 375
  11. 2022Transformers are Sample-Efficient World Modelscited by 328
  12. 2023Pretraining Language Models with Human Preferencescited by 247
22 more
  1. 2018RUDDER: Return Decomposition for Delayed Rewardscited by 173
  2. 2022Exploration in Deep Reinforcement Learning: A Surveycited by 166
  3. 2016Continuous Deep Q-Learning with Model-based Accelerationcited by 157
  4. 2018Model-Based Value Estimation for Efficient Model-Free Reinforcement Learningcited by 156
  5. 2017Visualizing and Understanding Atari Agentscited by 141
  6. 2023Transformer-based World Models Are Happy With 100k Interactionscited by 139
  7. 2020Explainable Deep Learning: A Field Guide for the Uninitiatedcited by 127
  8. 2021B-Pref: Benchmarking Preference-Based Reinforcement Learningcited by 106
  9. 2022Few-Shot Preference Learning for Human-in-the-Loop RLcited by 87
  10. 2019Hindsight Credit Assignmentcited by 79
  11. 2023Preference Transformer: Modeling Human Preferences using Transformers for RLcited by 79
  12. 2019Attention Interpretability Across NLP Taskscited by 71
  13. 2023Bridging the Gap: A Survey on Integrating (Human) Feedback for Natural Language Generationcited by 55
  14. 2021Towards Robust Bisimulation Metric Learningcited by 49
  15. 2021Augmented World Models Facilitate Zero-Shot Dynamics Generalization From a Single Offline Environmentcited by 42
  16. 2020Align-RUDDER: Learning From Few Demonstrations by Reward Redistributioncited by 36
  17. 2018Sparse Attentive Backtracking: Temporal CreditAssignment Through Remindingcited by 34
  18. 2021Learning Long-Term Reward Redistribution via Randomized Return Decompositioncited by 33
  19. 2018Recall Traces: Backtracking Models for Efficient Reinforcement Learningcited by 23
  20. 2019Reinforcement Learning with Attention that Works: A Self-Supervised Approachcited by 19
  21. 2019Visual Rationalizations in Deep Reinforcement Learning for Atari Gamescited by 8
  22. 2019Attention is all you need for Videos: Self-attention based Video Summarization using Universal Transformerscited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 5; 2025: 2; 2026: 5.

12 papers.

  1. 2025Reward Models in Deep Reinforcement Learning: A Surveycited by 25
  2. 2024Position: Towards Bidirectional Human-AI Alignmentcited by 18
  3. 2026HISR: Hindsight Information Modulated Segmental Process Rewards For Multi-turn Agentic Reinforcement Learningcited by 8
  4. 2024Leveraging Sub-Optimal Data for Human-in-the-Loop Reinforcement Learningcited by 6
  5. 2024Hindsight Preference Learning for Offline Preference-based Reinforcement Learningcited by 3
  6. 2024Multi-Type Preference Learning: Empowering Preference-Based Reinforcement Learning with Equal Preferencescited by 2
  7. 2026Crayotter: Learning Long-Horizon Video Editing Agents via Group-Relative Preference Backpropagationcited by 1
  8. 2024LEASE: Offline Preference-based Reinforcement Learning with High Sample Efficiencycited by 0
  9. 2025Search-Based Credit Assignment for Offline Preference-Based Reinforcement Learningcited by 0
  10. 2026The Trajectory Alignment Coefficient in Two Acts: From Reward Tuning to Reward Learningcited by 0
  11. 2026PAWS: Preference Learning with Advantage-Weighted Segmentscited by 0
  12. 2026CoRe: Combined Rewards with Vision-Language Model Feedback for Preference-Aligned Reinforcement Learningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2022Symbol Guided Hindsight Priors for Reward Learning from Human Preferences
  2. 2025Residual Reward Models: Leveraging Prior Knowledge for Efficient Preference-based Reinforcement Learning in Robotics
  3. 2026HiRE: Hindsight Reward Editing for Policy Finetuning ↗
  4. 2019Dueling Posterior Sampling for Preference-Based Reinforcement Learning
  5. 2023On The Fragility of Learned Reward Functions
  6. 2023A State Augmentation based approach to Reinforcement Learning from Human Preferences
  7. 2025Best Policy Learning from Trajectory Preference Feedback
  8. 2026Hindsight Preference Replay Improves Preference-Conditioned Multi-Objective Reinforcement Learning