Conditional Equivalence of DPO and RLHF: Implicit Assumption, Failure Modes, and Provable Alignment
Quick Answer
This paper establishes that Direct Preference Optimization (DPO) and Reinforcement Learning from Human Feedback (RLHF) are conditionally equivalent, hinging on the assumption that RLHF policies prefer human-preferred responses.
Quick Take
When this assumption fails, can lead to undesirable outcomes, prompting the introduction of Constrained Preference Optimization (CPO) for guaranteed alignment. Experiments show CPO achieves state-of-the-art performance on standard benchmarks.
Key Points
- DPO's equivalence to RLHF relies on the assumption of human-preferred responses.
- Failure of this assumption can lead to pathological convergence in DPO.
- CPO introduces constraints to ensure provable alignment with human preferences.
- Theoretical analysis clarifies when DPO guarantees hold.
- CPO demonstrates state-of-the-art performance on standard benchmarks.
Paper Resources
📖 Reader Mode
~2 min readAbstract: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 pathological convergence where policies decrease DPO loss while preferring dispreferred responses. We characterize when this assumption is violated, show the existence of an undesirable solution space, and prove that DPO and RLHF optimize fundamentally different objectives in such cases. To address this, we introduce Constrained Preference Optimization (CPO), augmenting RLHF with constraints for provable alignment. We further provide a geometric interpretation through soft margin ranking, revealing that DPO implements margin ranking with potentially negative targets. Our theoretical analysis establishes when DPOs' guarantees hold and provides solutions preserving simplicity with provable alignment. Comprehensive experiments on standard benchmarks demonstrate that CPO achieves state-of-the-art performance. Code is available at: this https URL.
| Comments: | 49 pages |
| Subjects: | Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.20834 [cs.AI] |
| (or arXiv:2605.20834v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2605.20834 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Zhiqin Yang [view email]
[v1]
Wed, 20 May 2026 07:26:22 UTC (740 KB)
— Originally published at arxiv.org
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