Governance Challenges in Reinforcement Learning from Human Feedback: Evaluator Rationality and Reinforcement Stability
This addresses governance challenges in AI alignment pipelines for large language models, but it is incremental as it focuses on improving existing RLHF processes.
The study tackled the problem of evaluator bias and inconsistency in Reinforcement Learning from Human Feedback (RLHF) by examining how evaluator rationality affects reinforcement stability, finding that high-rationality participants produced significantly more consistent and expert-aligned feedback with statistical significance (p < 0.01).
Reinforcement Learning from Human Feedback (RLHF) is central in aligning large language models (LLMs) with human values and expectations. However, the process remains susceptible to governance challenges, including evaluator bias, inconsistency, and the unreliability of feedback. This study examines how the cognitive capacity of evaluators, specifically their level of rationality, affects the stability of reinforcement signals. A controlled experiment comparing high-rationality and low-rationality participants reveals that evaluators with higher rationality scores produce significantly more consistent and expert-aligned feedback. In contrast, lower-rationality participants demonstrate considerable variability in their reinforcement decisions ($p < 0.01$). To address these challenges and improve RLHF governance, we recommend implementing evaluator pre-screening, systematic auditing of feedback consistency, and reliability-weighted reinforcement aggregation. These measures enhance the fairness, transparency, and robustness of AI alignment pipelines.