Intrinsic Mutual Information as a Modulator for Preference Optimization

• URL: http://arxiv.org/abs/2604.24804v1
• Date: Mon, 27 Apr 2026 05:22:54 GMT
• Title: Intrinsic Mutual Information as a Modulator for Preference Optimization
• Abstract summary: We propose RMiPO, a lightweight and efficient framework for offline preference optimization.<n>We show that RMiPO achieves consistently superior performance over existing methods while reducing training overhead by more than 15%.
• Score: 21.430166597980804
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Offline preference optimization methods, such as Direct Preference Optimization (DPO), offer significant advantages in aligning Large Language Models (LLMs) with human values. However, achieving optimal performance with these methods typically involves additional hyperparameter tuning, resulting in substantial time overhead. Although prior work has proposed a range of improvements, these methods remain limited in effectiveness and have not fully eliminated reliance on hyperparameter tuning. In this work, we propose RMiPO, a lightweight and efficient framework for offline preference optimization. RMiPO leverages intrinsic Response-level Mutual information for Preference Optimization with hyperparameter modulation, dynamically decoupling preference contributions at negligible additional computational cost. Extensive experimental results demonstrate that RMiPO achieves consistently superior performance over existing methods while reducing training overhead by more than 15\%. Our code is available at https://github.com/liavonpenn/rmipo.

Related papers

• Adaptive Preference Optimization with Uncertainty-aware Utility Anchor [33.74005997646761]
  offline preference optimization methods are efficient for large language models (LLMs) alignment.<n>We propose a general framework for offline preference optimization methods, which introduces an anchoring function to estimate the uncertainties brought from preference data annotation.<n>Our method enables training even in scenarios where the data is unpaired, significantly enhancing data utilization efficiency.
  arXiv  Detail & Related papers  (2025-09-03T10:20:08Z)
• MaPPO: Maximum a Posteriori Preference Optimization with Prior Knowledge [35.703451475662995]
  We propose Maximum a Posteriori Preference Optimization (MaPPO), a framework for learning from preferences.<n>MaPPO integrates prior reward estimates into a principled Maximum a Posteriori (MaP) objective.<n>MaPPO can be used as a plugin with consistent improvement on DPO variants.
  arXiv  Detail & Related papers  (2025-07-27T05:26:50Z)
• ASPO: Adaptive Sentence-Level Preference Optimization for Fine-Grained Multimodal Reasoning [14.034412856423529]
  Direct Preference Optimization (DPO) has gained attention for its simplicity and computational efficiency in aligning large language models (LLMs)<n>Recent advancements have extended DPO to multimodal scenarios, achieving strong performance.<n>Traditional DPO relies on binary preference optimization, rewarding or penalizing entire responses without considering fine-grained segment correctness.<n>We propose Adaptive Sentence-level Preference Optimization (ASPO), which evaluates individual sentences for more precise preference optimization.
  arXiv  Detail & Related papers  (2025-05-25T11:33:08Z)
• A Simple and Effective Reinforcement Learning Method for Text-to-Image Diffusion Fine-tuning [61.403275660120606]
  Reinforcement learning (RL)-based fine-tuning has emerged as a powerful approach for aligning diffusion models with black-box objectives.<n>We propose leave-one-out PPO (LOOP), a novel RL for diffusion fine-tuning method.<n>Our results demonstrate that LOOP effectively improves diffusion models on various black-box objectives, and achieves a better balance between computational efficiency and performance.
  arXiv  Detail & Related papers  (2025-03-02T13:43:53Z)
• Accelerated Preference Optimization for Large Language Model Alignment [60.22606527763201]
  Reinforcement Learning from Human Feedback (RLHF) has emerged as a pivotal tool for aligning large language models (LLMs) with human preferences. Direct Preference Optimization (DPO) formulates RLHF as a policy optimization problem without explicitly estimating the reward function. We propose a general Accelerated Preference Optimization (APO) framework, which unifies many existing preference optimization algorithms.
  arXiv  Detail & Related papers  (2024-10-08T18:51:01Z)
• 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)
• Multi-Reference Preference Optimization for Large Language Models [56.84730239046117]
  We introduce a novel closed-form formulation for direct preference optimization using multiple reference models. The resulting algorithm, Multi-Reference Preference Optimization (MRPO), leverages broader prior knowledge from diverse reference models. Our experiments demonstrate that LLMs finetuned with MRPO generalize better in various preference data, regardless of data scarcity or abundance.
  arXiv  Detail & Related papers  (2024-05-26T00:29:04Z)
• Towards Efficient Exact Optimization of Language Model Alignment [93.39181634597877]
  Direct preference optimization (DPO) was proposed to directly optimize the policy from preference data. We show that DPO derived based on the optimal solution of problem leads to a compromised mean-seeking approximation of the optimal solution in practice. We propose efficient exact optimization (EXO) of the alignment objective.
  arXiv  Detail & Related papers  (2024-02-01T18:51:54Z)
• Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization [71.87335804334616]
  Federated learning (FL) is a promising paradigm to enable collaborative model training with decentralized data. The training process of Large Language Models (LLMs) generally incurs the update of significant parameters. This paper proposes an efficient partial prompt tuning approach to improve performance and efficiency simultaneously.
  arXiv  Detail & Related papers  (2023-10-23T16:37:59Z)

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.