Reliability-Aware LLM Alignment from Inconsistent Human Feedback
Reinforcement Learning from Human Feedback (RLHF) is critical for aligning Large Language Models (LLMs) with human preferences. However, its efficacy is often compromised by the inherent inconsistency and subjectivity of human annotations. Existing preference optimization frameworks, such as Direct Preference Optimization (DPO), typically treat ambiguous pairs with high annotator disagreement identically to those with unanimous consensus, forcing models to overfit to inconsistent supervision signals and leading to suboptimal alignment. In this work, we propose Reliability-Guided Preference Optimization (RGPO), a robust framework designed to mitigate the impact of inconsistent human feedba...
| Authors | Jingyi Huang, Ruohan Zong, Yujun Feng et al. |
|---|---|
| Primary category | cs.AI |
| Published | 7 July 2026 |
| Last updated at source | 7 July 2026 |
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