Wasserstein Distributionally Robust Regret Optimization for Reinforcement Learning from Human Feedback
arXiv:2605.00155v3 Announce Type: replace-cross Abstract: Reinforcement learning from human feedback (RLHF) is a central post-training tool for aligning large language models, but its training reward is only a learned proxy for true human utility. This creates a decision problem under objective misspecification: the policy is optimized against an estimated reward, while deployment performance is governed by an unobserved population preference. The resulting gap leads to reward over-optimization, where proxy reward keeps improving after true quality deteriorates. We propose distributionally rob
The increasing sophistication and widespread deployment of large language models are exposing fundamental limitations in current alignment techniques, driving research into more robust optimization methods.
Improving the robustness of AI alignment is critical for ensuring that AI systems reliably serve human intentions, preventing unintended negative consequences as models become more autonomous and powerful.
This research provides a theoretical and practical framework to mitigate 'reward over-optimization' in RLHF, leading to more stable and trustworthy AI development.
- · AI developers
- · Large Language Models (LLMs)
- · AI safety researchers
- · End-users of AI systems
- · Developers relying on naive RLHF
- · Companies facing AI alignment failures
More reliable and less 'quirky' behavior from advanced AI models, especially large language models.
Increased user trust and adoption of AI systems in sensitive or critical applications due to improved safety and alignment.
Accelerated progress towards general AI agents that can operate more autonomously without constant human oversight.
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Read at arXiv cs.CL