Mind the Gap: Structure-Aware Consistency in Preference Learning
Aligning Large Language Models (LLMs) with human intent, whether through explicit reward modeling or direct methods such as DPO, fundamentally relies on minimizing a surrogate loss as a proxy for the true pairwise ranking objective. We prove that this reliance is flawed for the standard surrogate losses used: for the equicontinuous hypothesis sets characteristic of neural networks, no standard surrogate provides a meaningful consistency guarantee. Minimizing the surrogate loss to zero can leave the true ranking error arbitrarily high. To resolve this, we formulate LLM alignment within a margin-shifted ranking framework and derive $H$-consistency bounds showing that enforcing a confidence ...
| Authors | Mehryar Mohri, Yutao Zhong |
|---|---|
| Primary category | cs.LG |
| Published | 30 April 2026 |
| Last updated at source | 15 October 2026 |
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