Conditional Equivalence of DPO and RLHF: Implicit Assumption, Failure Modes, and Provable Alignment
arXiv:2605.20834v1 Announce Type: cross Abstract: Direct Preference Optimization (DPO) has emerged as a popular alternative to Reinforcement Learning from Human Feedback (RLHF), offering theoretical equivalence with simpler implementation. We prove this equivalence is conditional rather than universal, depending on an implicit assumption frequently violated in practice: the RLHF-optimal policy must prefer human-preferred responses. When this assumption fails, DPO optimizes relative advantage over the reference policy rather than absolute alignment with human preferences, leading to pathologica
The rapid advancement and deployment of large language models have brought preference optimization techniques like DPO and RLHF to the forefront of AI alignment research and application.
This research highlights a critical, previously unacknowledged limitation in a widely adopted AI alignment technique (DPO), impacting trust, reliability, and safety of AI systems.
The understanding of DPO's theoretical guarantees is now conditional, demanding more rigorous validation and potentially necessitating new or refined alignment methodologies.
- · AI safety researchers
- · Developers of new alignment algorithms
- · Companies investing in robust AI verification methods
- · Developers solely relying on DPO for alignment
- · Products deployed with insufficiently validated DPO-aligned models
- · AI initiatives without strong alignment verification
Increased scrutiny and re-evaluation of currently deployed AI models aligned using DPO.
Accelerated research into more robust and universally applicable AI alignment techniques beyond conditional DPO.
Potential for a 'trust crisis' in certain AI applications if discovered alignment failures lead to significant real-world harms.
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Read at arXiv cs.LG