Factored Causal Representation Learning for Robust Reward Modeling in RLHF

TL;DR

This paper introduces a causal factorization approach to improve reward models in RLHF, reducing reward hacking by isolating causal features and suppressing non-causal biases.

Contribution

It proposes a novel factored representation learning framework that enhances reward model robustness by disentangling causal and non-causal factors from contextual embeddings.

Findings

Learned more robust reward models in mathematical and dialogue tasks.

Improved downstream RLHF performance over state-of-the-art baselines.

Effectively mitigated reward hacking behaviors related to length and bias.

Abstract

A reliable reward model is essential for aligning large language models with human preferences through reinforcement learning from human feedback. However, standard reward models are susceptible to spurious features that are not causally related to human labels. This can lead to reward hacking, where high predicted reward does not translate into better behavior. In this work, we address this problem from a causal perspective by proposing a factored representation learning framework that decomposes the model's contextual embedding into (1) causal factors that are sufficient for reward prediction and (2) non-causal factors that capture reward-irrelevant attributes such as length or sycophantic bias. The reward head is then constrained to depend only on the causal component. In addition, we introduce an adversarial head trained to predict reward from the non-causal factors, while applying gradient reversal to discourage them from encoding reward-relevant information. Experiments on both mathematical and dialogue tasks demonstrate that our method learns more robust reward models and consistently improves downstream RLHF performance over state-of-the-art baselines. Analyses on length and sycophantic bias further validate the effectiveness of our method in mitigating reward hacking behaviors.