mlsanityData to 14 Oct 2026

STAIR: Addressing Stage Misalignment through Temporal-Aligned Preference Reinforcement Learning

arXiv 2509.23802 ↗First posted 28 Sep 2025cs.LGcited by 3

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

also in 1 more
Abstract (arXiv)

Preference-based reinforcement learning (PbRL) bypasses complex reward engineering by learning rewards directly from human preferences, enabling better alignment with human intentions. However, its effectiveness in multi-stage tasks, where agents sequentially perform sub-tasks (e.g., navigation, grasping), is limited by stage misalignment: Comparing segments from mismatched stages, such as movement versus manipulation, results in uninformative feedback, thus hindering policy learning. In this paper, we validate the stage misalignment issue through theoretical analysis and empirical experiments. To address this issue, we propose STage-AlIgned Reward learning (STAIR), which first learns a stage approximation based on temporal distance, then prioritizes comparisons within the same stage. Temporal distance is learned via contrastive learning, which groups temporally close states into coherent stages, without predefined task knowledge, and adapts dynamically to policy changes. Extensive experiments demonstrate STAIR's superiority in multi-stage tasks and competitive performance in single-stage tasks. Furthermore, human studies show that stages approximated by STAIR are consistent with human cognition, confirming its effectiveness in mitigating stage misalignment.

Key facts. Cited by 3 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 35 of its 48 references that are in the map. First posted on arXiv 28 Sep 2025.

Builds on

35 of 48 references.

Closest ancestors · 8

Same direction, newest first

  1. 2024Efficient Preference-based Reinforcement Learning via Aligned Experience Estimation29 May
  2. 2023Benchmarks and Algorithms for Offline Preference-Based Reward Learning3 Jan
  3. 2022Reward Uncertainty for Exploration in Preference-based Reinforcement Learning24 May
  4. 2022SURF: Semi-supervised Reward Learning with Data Augmentation for Feedback-efficient Preference-based Reinforcement Learning18 Mar
  5. 2021PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training9 Jun
  6. 2020Active Preference-Based Gaussian Process Regression for Reward Learning6 May
  7. 2018Reward learning from human preferences and demonstrations in Atari15 Nov
  8. 2017Deep reinforcement learning from human preferences12 Jun

Other references · 27

  1. 2018Representation Learning with Contrastive Predictive Codingcited by 7,777
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  3. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  4. 2019Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learningcited by 1,204
  5. 2018DeepMind Control Suitecited by 956
  6. 2019On Variational Bounds of Mutual Informationcited by 559
  7. 2017Time-Contrastive Networks: Self-Supervised Learning from Videocited by 459
  8. 2017Automatic Goal Generation for Reinforcement Learning Agentscited by 259
  9. 2022Contrastive Learning as Goal-Conditioned Reinforcement Learningcited by 253
  10. 2019VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learningcited by 217
  11. 2022Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learningcited by 131
  12. 2023Optimal Goal-Reaching Reinforcement Learning via Quasimetric Learningcited by 121
15 more
  1. 2022Towards Human-Level Bimanual Dexterous Manipulation with Reinforcement Learningcited by 119
  2. 2021B-Pref: Benchmarking Preference-Based Reinforcement Learningcited by 106
  3. 2022Few-Shot Preference Learning for Human-in-the-Loop RLcited by 87
  4. 2018The Laplacian in RL: Learning Representations with Efficient Approximationscited by 86
  5. 2023METRA: Scalable Unsupervised RL with Metric-Aware Abstractioncited by 86
  6. 2023Preference Transformer: Modeling Human Preferences using Transformers for RLcited by 79
  7. 2024Learning Temporal Distances: Contrastive Successor Features Can Provide a Metric Structure for Decision-Makingcited by 56
  8. 2024RIME: Robust Preference-based Reinforcement Learning with Noisy Preferencescited by 39
  9. 2025Episodic Novelty Through Temporal Distancecited by 16
  10. 2023Query-Policy Misalignment in Preference-Based Reinforcement Learningcited by 15
  11. 2023Multi-Stage Reinforcement Learning for Non-Prehensile Manipulationcited by 7
  12. 2025Preference-based Multi-Objective Reinforcement Learningcited by 7
  13. 2023No Prior Mask: Eliminate Redundant Action for Deep Reinforcement Learningcited by 6
  14. 2024S-EPOA: Overcoming the Indistinguishability of Segments with Skill-Driven Preference-Based Reinforcement Learningcited by 4
  15. 2025CLARIFY: Contrastive Preference Reinforcement Learning for Untangling Ambiguous Queriescited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026COLLIE: Guiding Skill Discovery in Semantically Coherent Latent Spacecited by 1
  2. 2026From Reward-Free Representations to Preferences: Rethinking Offline Preference-Based Reinforcement Learningcited by 1
  3. 2026Multi-Agent Reinforcement Learning via Agent-Specific Preferencecited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Aligning Agents via Planning: A Benchmark for Trajectory-Level Reward Modeling
  2. 2025Similarity as Reward Alignment: Robust and Versatile Preference-based Reinforcement Learning
  3. 2025Towards Improving Reward Design in RL: A Reward Alignment Metric for RL Practitioners
  4. 2026PAWS: Preference Learning with Advantage-Weighted Segments
  5. 2024Learning Reward and Policy Jointly from Demonstration and Preference Improves Alignment
  6. 2025Robust Reward Alignment via Hypothesis Space Batch Cutting
  7. 2026The Trajectory Alignment Coefficient in Two Acts: From Reward Tuning to Reward Learning
  8. 2025From Demonstrations to Rewards: Alignment Without Explicit Human Preferences