g-DPO: Scalable Preference Optimization for Protein Language Models

• URL: http://arxiv.org/abs/2510.19474v1
• Date: Wed, 22 Oct 2025 11:11:42 GMT
• Title: g-DPO: Scalable Preference Optimization for Protein Language Models
• Authors: Constance Ferragu, Jonathan D. Ziegler, Nicolas Deutschmann, Arthur Lindoulsi, Eli Bixby, Cradle ML Team,
• Abstract summary: g-DPO is a framework that prunes redundant pairs while preserving training signal, and (ii) amortizes likelihood computations with group-based approximations.<n>Across three protein engineering tasks, g-DPO maintains in-silico and in-vitro performance that is statistically indistinguishable from standard DPO, while converging 1.8 to 3.7 times faster.
• Score: 0.9236074230806578
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Direct Preference Optimization (DPO) is an effective approach for aligning protein language models with experimental design goals. However, DPO faces a scalability bottleneck: the number of possible training pairs grows quadratically with the number of labeled sequences, leading to prohibitive training times even for modestly sized datasets. We introduce g-DPO, a framework that (i) uses sequence space clustering to prune redundant pairs while preserving training signal, and (ii) amortizes likelihood computations with group-based approximations. Across three protein engineering tasks, g-DPO maintains in-silico and in-vitro performance that is statistically indistinguishable from standard DPO, while converging 1.8 to 3.7 times faster, with greater gains expected as the size of the dataset increases.

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