Provably avoiding over-optimization in Direct Preference Optimization without knowing the data distribution

• URL: http://arxiv.org/abs/2602.06239v1
• Date: Thu, 05 Feb 2026 22:31:07 GMT
• Title: Provably avoiding over-optimization in Direct Preference Optimization without knowing the data distribution
• Authors: Adam Barla, Emanuele Nevali, Luca Viano, Volkan Cevher,
• Abstract summary: We introduce PEPO, a single-step Direct Preference Optimization-like algorithm to mitigate the well-known over-optimization issue in preference learning.<n> PEPO achieves pessimism via an ensemble of preference-optimized policies trained on disjoint data subsets.
• Score: 47.604070468150844
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce PEPO (Pessimistic Ensemble based Preference Optimization), a single-step Direct Preference Optimization (DPO)-like algorithm to mitigate the well-known over-optimization issue in preference learning without requiring the knowledge of the data-generating distribution or learning an explicit reward model. PEPO achieves pessimism via an ensemble of preference-optimized policies trained on disjoint data subsets and then aggregates them through a worst case construction that favors the agreement across models. In the tabular setting, PEPO achieves sample complexity guarantees depending only on a single-policy concentrability coefficient, thus avoiding the all-policy concentrability which affects the guarantees of algorithms prone to over-optimization, such as DPO. The theoretical findings are corroborated by a convincing practical performance, while retaining the simplicity and the practicality of DPO-style training.

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