arXiv:2606.09124v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has enabled progress on reasoning-intensive tasks by relying on task-specific verifiers that provide automated correctness signals. However, many realistic language tasks are difficult to equip with reliable verifiers, motivating a growing reliance on reinforcement learning from human feedback (RLHF). In this setting, we argue that a closer examination of how human feedback should be interpreted is essential. We introduce Regret-based Preference Optimization $(\textbf{RePO})$, which reframes R
The increasing reliance on human feedback for complex AI tasks highlights the need for more robust and refined preference optimization methods as AI models become more sophisticated.
This work introduces a new framework for preference learning in LLMs that could lead to more nuanced and effective human-AI alignment, improving the reliability and utility of AI agents.
The proposed RePO framework reframes how human feedback is interpreted, potentially leading to more accurate and efficient training of LLMs, especially in tasks where explicit correctness signals are absent.
- · AI developers
- · LLM researchers
- · AI service providers
- · Inefficient RLHF methods
- · Developers relying solely on 'verifiable rewards'
Improved performance and alignment of large language models in complex, subjective tasks.
Accelerated development of more capable and reliable AI agents for diverse applications.
Enhanced trust and adoption of AI systems due to better interpretation of human intent and preferences.
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Read at arXiv cs.AI