GroupDPO: Memory-Efficient Group-Wise Direct Preference Optimization
Preference optimization is widely used to align Large Language Models (LLMs) with preference feedback. However, most existing methods train on a single positive-negative pair per prompt, discarding additional supervision available in preference datasets that typically contain multiple candidate responses. Motivated by this limitation, recent work explores group-wise preference optimization, which jointly contrasts multiple responses for the same prompt, but its empirical behavior and scalability remain underexplored due to the memory overhead of group-coupled objectives. In this work, we present a unified empirical and systems study of group-wise preference optimization and develop a memo...
| Authors | Jixuan Leng, Si Si, Hsiang-Fu Yu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 17 April 2026 |
| Last updated at source | 1 September 2026 |
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