arXiv:2606.19818v1 Announce Type: new Abstract: Reinforcement learning from human feedback (RLHF) aligns large language models by training reward models on preference data and optimizing policies to maximize predicted rewards. However, this pipeline faces two fundamental challenges: (1) reward models cannot signal when their predictions are unreliable, since they usually act as deterministic point estimators; and (2) modern group-based policy optimization can amplify unreliable reward signals, as exemplified by GRPO's uniform treatment of rewards during advantage computation. As policies explo
The rapid advancement of RLHF in large language models necessitates addressing fundamental issues like reward model reliability to ensure stable and effective policy optimization.
Improved stability and interpretability in RLHF are crucial for the safe and robust deployment of advanced AI systems, particularly autonomous agents and large language models, impacting their trustworthiness and applicability.
This research introduces methods to make reward models uncertainty-aware, potentially leading to more reliable AI training and reducing the risk of unintended consequences in AI-driven systems by preventing amplification of unreliable signals.
- · AI researchers
- · Developers of autonomous AI agents
- · Users of large language models
- · AI safety and ethics organizations
- · Developers relying on deterministic reward models
- · AI systems prone to reward hacking
More stable and predictable performance from AI systems trained with RLHF.
Increased trust and adoption of AI-powered applications in critical domains.
Acceleration of research into more sophisticated human-AI alignment techniques and agentic systems.
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Read at arXiv cs.LG