GroupDPO: Memory efficient Group-wise Direct Preference Optimization

• URL: http://arxiv.org/abs/2604.15602v1
• Date: Fri, 17 Apr 2026 00:56:59 GMT
• Title: GroupDPO: Memory efficient Group-wise Direct Preference Optimization
• Abstract summary: We introduce a memory-efficient group-wise preference optimization algorithm that preserves gradients while decoupling samples during backpropagation.<n>Across both offline and online alignment settings, we show that leveraging multiple responses consistently outperforms single-pair training.
• Score: 29.469340103121144
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 introduce a memory-efficient group-wise preference optimization algorithm that preserves gradients while decoupling samples during backpropagation, substantially reducing peak memory usage, which enables scalable training with larger group sizes. Across both offline and online alignment settings, we show that leveraging multiple responses consistently outperforms single-pair training. Furthermore, incorporating a negative log-likelihood (NLL) term on positive responses is critical for both performance gains and training stability.

Related papers

• Optimizing Sparse Outcomes Through Dense Behavioral Signals via Value-Guided Preference Distillation [5.277024349608834]
  We formulate long-horizon dialogue optimization as a multi-objective reinforcement learning problem.<n>We show that a scalarized composite of dense auxiliary behavioral signals enables effective credit assignment and optimization of sparse outcomes.<n>We demonstrate that our distilled policy significantly improves long-term user retention, while simultaneously enhancing the positive behaviors and therapeutic-process markers.
  arXiv  Detail & Related papers  (2026-09-13T16:32:54Z)
• MASS-DPO: Multi-negative Active Sample Selection for Direct Policy Optimization [66.81594613265833]
  Multi-negative preference optimization under the Plackett--Luce (PL) model extends Direct Preference Optimization (DPO)<n>We introduce MASS-DPO, a multi-negative active sample selection method that derives a PL-specific Fisher-information objective for selecting compact, informative negative subsets within each prompt.<n> MASS-DPO consistently exceeds or matches existing methods in accuracy, improves Recall/NDCG and margin-based optimization dynamics, and delivers stronger alignment with substantially fewer negatives.
  arXiv  Detail & Related papers  (2026-05-11T16:18:08Z)
• GDPO: Group reward-Decoupled Normalization Policy Optimization for Multi-reward RL Optimization [133.27496265096445]
  We show how to apply Group Relative Policy Optimization under multi-reward setting without examining its suitability.<n>We then introduce Group reward-Decoupled Normalization Policy Optimization (GDPO), a new policy optimization method to resolve these issues.<n>GDPO consistently outperforms GRPO, demonstrating its effectiveness and generalizability for multi-reward reinforcement learning optimization.
  arXiv  Detail & Related papers  (2026-01-08T18:59:24Z)
• Beyond Single: A Data Selection Principle for LLM Alignment via Fine-Grained Preference Signals [46.58760908162995]
  We propose a novel, theoretically-grounded data selection principle for large language models.<n>We prove the optimality of this strategy by analyzing the loss bounds of the Direct Preference Optimization objective.<n>Our strategy achieves over 10% relative improvement against both the standard holistic preference and a stronger oracle.
  arXiv  Detail & Related papers  (2025-08-11T05:43:02Z)
• AMPO: Active Multi-Preference Optimization for Self-play Preference Selection [16.230186347702737]
  Multi-preference optimization enriches language-model alignment beyond pairwise preferences by contrasting entire sets of helpful and undesired responses.<n>We propose $textitActive Multi-Preference Optimization$ (AMPO), a novel approach that combines on-policy generation, a multi-preference group-contrastive loss, and active subset selection.<n>AMPO achieves state-of-the-art results on $textitAlpacaEval$ using Llama 8B and Mistral Mist 7B.
  arXiv  Detail & Related papers  (2025-02-25T15:29:51Z)
• Self-Improvement Towards Pareto Optimality: Mitigating Preference Conflicts in Multi-Objective Alignment [74.25832963097658]
  Multi-Objective Alignment (MOA) aims to align responses with multiple human preference objectives.<n>We find that DPO-based MOA approaches suffer from widespread preference conflicts in the data.
  arXiv  Detail & Related papers  (2025-02-20T08:27:00Z)
• Multi-Preference Optimization: Generalizing DPO via Set-Level Contrasts [17.243429150450886]
  We propose $textbfMulti-Preference Optimization (MPO) to optimize over entire sets of responses.<n>MPO employs deviation-based weighting, which emphasizes outlier responses that deviate most from the mean reward.<n>We theoretically prove that MPO reduces alignment bias at a rate of $mathcalOleft(frac1sqrtnright)$ with respect to the number of responses per query.
  arXiv  Detail & Related papers  (2024-12-05T21:50:22Z)
• Self-supervised Preference Optimization: Enhance Your Language Model with Preference Degree Awareness [27.43137305486112]
  We propose a novel Self-supervised Preference Optimization (SPO) framework, which constructs a self-supervised preference degree loss combined with the alignment loss. The results demonstrate that SPO can be seamlessly integrated with existing preference optimization methods to achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-09-26T12:37:26Z)
• Bridging and Modeling Correlations in Pairwise Data for Direct Preference Optimization [75.1240295759264]
  We propose an effective framework for Bridging and Modeling Correlations in pairwise data, named BMC.<n>We increase the consistency and informativeness of the pairwise preference signals through targeted modifications.<n>We identify that DPO alone is insufficient to model these correlations and capture nuanced variations.
  arXiv  Detail & Related papers  (2024-08-14T11:29:47Z)
• Adaptive Preference Scaling for Reinforcement Learning with Human Feedback [103.36048042664768]
  Reinforcement learning from human feedback (RLHF) is a prevalent approach to align AI systems with human values. We propose a novel adaptive preference loss, underpinned by distributionally robust optimization (DRO) Our method is versatile and can be readily adapted to various preference optimization frameworks.
  arXiv  Detail & Related papers  (2024-06-04T20:33:22Z)
• Comparing Bad Apples to Good Oranges: Aligning Large Language Models via Joint Preference Optimization [105.3612692153615]
  We propose a new axis based on eliciting preferences jointly over instruction-response pairs.<n>Joint preferences over instruction and response pairs can significantly enhance the alignment of large language models.
  arXiv  Detail & Related papers  (2024-03-31T02:05:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.