Mitigating Cognitive Bias in RLHF by Altering Rationality

TL;DR

This paper proposes a method to improve reinforcement learning from human feedback by dynamically adjusting the rationality parameter to account for cognitive biases in human judgments, leading to more reliable models.

Contribution

It introduces a novel approach to adaptively modify the rationality parameter during reward learning using an LLM to detect biases, enhancing model robustness.

Findings

The method produces more rational models despite biased datasets.

Dynamic adjustment of the rationality parameter improves reward model accuracy.

The approach effectively identifies and downweights biased human preferences.

Abstract

How can we make models robust to even imperfect human feedback? In reinforcement learning from human feedback (RLHF), human preferences over model outputs are used to train a reward model that assigns scalar values to responses. Because these rewards are inferred from pairwise comparisons, this learning depends on an assumed relationship between latent reward differences and observed preferences, typically modeled using a Boltzmann formulation in which a rationality parameter beta informs how consistently preferences reflect reward differences. In practice, beta is typically treated as a fixed constant that reflects assumed uniform annotator reliability. However, human feedback is not this simplistic in practice: real human judgments are shaped by cognitive biases, leading to systematic deviations from reward-consistent behavior that arise contextually. To address this, we treat rationality as context- and annotation-dependent. We design an approach to dynamically adjust the rationality parameter beta during reward learning using an LLM-as-judge to assess the likely presence of cognitive biases. This approach effectively downweights comparisons that are likely to reflect biased or unreliable judgments. Empirically, we show that this approach learns a more rational downstream model, even when finetuning on datasets with strongly biased preferences.