Reward Model Interpretability via Optimal and Pessimal Tokens

• URL: http://arxiv.org/abs/2506.07326v1
• Date: Sun, 08 Jun 2025 23:56:58 GMT
• Title: Reward Model Interpretability via Optimal and Pessimal Tokens
• Authors: Brian Christian, Hannah Rose Kirk, Jessica A. F. Thompson, Christopher Summerfield, Tsvetomira Dumbalska,
• Abstract summary: Reward modeling has emerged as a crucial component in aligning large language models with human values.<n>We present a novel approach to reward model interpretability through exhaustive analysis of their responses across their entire vocabulary space.<n>We find that these models can encode concerning biases toward certain identity groups, which may emerge as unintended consequences of harmlessness training.
• Score: 4.951383975460995
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reward modeling has emerged as a crucial component in aligning large language models with human values. Significant attention has focused on using reward models as a means for fine-tuning generative models. However, the reward models themselves -- which directly encode human value judgments by turning prompt-response pairs into scalar rewards -- remain relatively understudied. We present a novel approach to reward model interpretability through exhaustive analysis of their responses across their entire vocabulary space. By examining how different reward models score every possible single-token response to value-laden prompts, we uncover several striking findings: (i) substantial heterogeneity between models trained on similar objectives, (ii) systematic asymmetries in how models encode high- vs low-scoring tokens, (iii) significant sensitivity to prompt framing that mirrors human cognitive biases, and (iv) overvaluation of more frequent tokens. We demonstrate these effects across ten recent open-source reward models of varying parameter counts and architectures. Our results challenge assumptions about the interchangeability of reward models, as well as their suitability as proxies of complex and context-dependent human values. We find that these models can encode concerning biases toward certain identity groups, which may emerge as unintended consequences of harmlessness training -- distortions that risk propagating through the downstream large language models now deployed to millions.

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