Reward Model Perspectives: Whose Opinions Do Reward Models Reward?

• URL: http://arxiv.org/abs/2510.06391v1
• Date: Tue, 07 Oct 2025 19:13:52 GMT
• Title: Reward Model Perspectives: Whose Opinions Do Reward Models Reward?
• Abstract summary: Reward models (RMs) are central to the alignment of language models (LMs)<n>Our work formalizes a framework for measuring the alignment of opinions captured by RMs.<n>We show that RMs are poorly aligned with several demographic groups and can systematically reward harmful stereotypes.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reward models (RMs) are central to the alignment of language models (LMs). An RM often serves as a proxy for human preferences to guide downstream LM behavior. However, our understanding of RM behavior is limited. Our work (i) formalizes a framework for measuring the alignment of opinions captured by RMs, (ii) investigates the extent to which RMs demonstrate sociodemographic biases, and (iii) explores the effects of prompting to steer rewards towards the preferences of a target group. We study the subjective and diverse perspectives on controversial topics, which allows us to quantify RM perspectives in terms of their opinions, attitudes, and values. We show that RMs are poorly aligned with several demographic groups and can systematically reward harmful stereotypes, and steering alone is not enough to overcome these limitations. Our findings underscore the need for more careful consideration of RM behavior in model alignment during preference learning to prevent the propagation of unwanted social biases in the language technologies that we use.

Related papers

• One Bias After Another: Mechanistic Reward Shaping and Persistent Biases in Language Reward Models [32.15734141265721]
  Reward models (RMs) are crucial for online alignment of language models with human preferences.<n>We measure biases in five high-quality RMs, including the state-of-the-art.<n>We propose a simple post-hoc intervention to mitigate low-complexity biases that arise from spurious correlations.
  arXiv  Detail & Related papers  (2026-02-06T00:11:37Z)
• Reward Models Inherit Value Biases from Pretraining [4.004014851264611]
  Reward models (RMs) are central to aligning large language models with human values.<n>We show that RMs exhibit significant differences along multiple dimensions of human value as a function of their base model.<n>This work underscores the importance of safety and alignment efforts at the pretraining stage.
  arXiv  Detail & Related papers  (2026-01-28T18:40:29Z)
• Off-Policy Corrected Reward Modeling for Reinforcement Learning from Human Feedback [52.1410307583181]
  We useReinforcement Learning from Human Feedback to train language models (LMs) to follow complex human preferences.<n>As training progresses, the responses generated by the LM no longer resemble the responses seen by the reward model (RM)<n>We propose Off-Policy Corrected Reward Modeling to correct the RM using importance weighting, without requiring new labels or samples.
  arXiv  Detail & Related papers  (2025-07-21T11:19:04Z)
• Socratic-PRMBench: Benchmarking Process Reward Models with Systematic Reasoning Patterns [79.42805969325036]
  Process Reward Models (PRMs) are crucial in complex reasoning and problem-solving tasks.<n>PRMs are required to identify errors under various reasoning patterns during the reasoning process.<n>Existing benchmarks mainly focus on evaluating PRMs with stepwise correctness.<n>We introduce Socratic-PRMBench, a new benchmark to evaluate PRMs systematically under six reasoning patterns.
  arXiv  Detail & Related papers  (2025-05-29T14:26:53Z)
• Two Minds Better Than One: Collaborative Reward Modeling for LLM Alignment [35.80989342492335]
  noisy preferences in human feedback can lead to reward misgeneralization.<n>This paper aims to identify how noisy preferences differ from human-aligned preferences in reward modeling.<n>We propose an online Collaborative Reward Modeling framework to achieve robust preference learning.
  arXiv  Detail & Related papers  (2025-05-15T10:58:20Z)
• RM-R1: Reward Modeling as Reasoning [81.50471199906738]
  Reasoning Reward Models (ReasRMs) formulate reward modeling as a reasoning task.<n>We propose a reasoning-oriented training pipeline and train a family of ReasRMs, RM-R1.<n>Our models achieve state-of-the-art performance across three reward model benchmarks on average.
  arXiv  Detail & Related papers  (2025-05-05T06:11:12Z)
• Reward Models Identify Consistency, Not Causality [54.987590763737145]
  State-of-the-art reward models prioritize structural consistency over causal correctness.<n>Removing the problem statement has minimal impact on reward scores.<n> altering numerical values or disrupting the reasoning flow significantly affects RM outputs.
  arXiv  Detail & Related papers  (2025-02-20T14:57:14Z)
• RMB: Comprehensively Benchmarking Reward Models in LLM Alignment [44.84304822376291]
  Reward models (RMs) guide the alignment of large language models (LLMs)<n>We propose RMB, a comprehensive RM benchmark that covers over 49 real-world scenarios.<n>Based on our benchmark, we conduct extensive analysis on the state-of-the-art RMs.
  arXiv  Detail & Related papers  (2024-10-13T16:06:54Z)
• RRM: Robust Reward Model Training Mitigates Reward Hacking [51.12341734942797]
  Reward models (RMs) play a pivotal role in aligning large language models with human preferences.<n>We introduce a causal framework that learns preferences independent of these artifacts.<n>Experiments show that our approach successfully filters out undesirable artifacts, yielding a more robust reward model.
  arXiv  Detail & Related papers  (2024-09-20T01:46:07Z)
• Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-Experts [23.27203570485055]
  Reinforcement learning from human feedback (RLHF) has emerged as the primary method for aligning large language models with human preferences. We propose a two-stage approach to train a reward model (RM) with multi-dimensional absolute-rating data. We efficiently trained an ArmoRM with Llama-3 8B and a gating network consisting of a shallow on top of the ArmoRM.
  arXiv  Detail & Related papers  (2024-06-18T17:58:28Z)
• Confronting Reward Model Overoptimization with Constrained RLHF [114.71591361764547]
  We show that correlation between component RMs has a significant effect on the locations of these points. Our method addresses the problem of weighting component RMs by learning dynamic weights, naturally expressed by Lagrange multipliers.
  arXiv  Detail & Related papers  (2023-10-06T16:59:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.