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文章背景与核心概要

奖励建模是强化学习人类反馈（RLHF）、RLAIF以及基于PPO的训练等对齐技术的基础组成部分。然而，其有效性经常受到高质量人类偏好数据稀缺性和异构性的制约。本文介绍了一种名为MARS（Margin and Semantic-Aware Data Augmentation，边界与语义感知数据增强）的自适应框架，旨在优化低资源环境下的奖励建模。

MARS框架引入了两项核心创新：一是自适应分配（Adaptive Allocation），优先对低边界的偏好对进行数据增强，将计算资源集中在最需要的地方；二是语义精炼（Semantic Refinement），在生成合成训练样本之前，利用基于语义距离的精炼来增强“已选”与“拒绝”响应之间的对比度。在三个偏好数据集和两个奖励模型骨干网络上的实证结果表明，MARS在性能上始终优于标准的均匀增强、WoN以及AdaBoost风格的基线，从而带来了更高的RewardBench得分和更强的对齐胜率。

MARS: Margin and Semantic-Aware Data Augmentation for Reward Modeling

Authors: Payel Bhattacharjee, Osvaldo Simeone, Ravi Tandon
arXiv ID: 2602.17658
Primary Subject: Machine Learning (cs.LG)
Last Revised: August 5, 2026

Authors: Payel Bhattacharjee, Osvaldo Simeone, Ravi Tandon
arXiv ID: 2602.17658
Primary Subject: Machine Learning (cs.LG)
Last Revised: August 5, 2026

Summary

奖励建模是强化学习人类反馈（RLHF）、RLAIF以及基于PPO训练等对齐技术的基础组成部分。然而，其有效性经常受到高质量人类偏好数据稀缺性和异构性的阻碍。

Reward modeling is a foundational component of alignment techniques such as RLHF (Reinforcement Learning from Human Feedback), RLAIF, and PPO-based training. However, its effectiveness is frequently hampered by the scarcity and heterogeneity of high-quality human preference data.

MARS（边界与语义感知数据增强，Margin and Semantic-Aware Data Augmentation）是一个旨在优化低资源环境中奖励建模的自适应框架。该框架引入了两项关键创新： * 自适应分配： 优先对低边界的偏好对进行数据增强，将计算资源集中在最需要的地方。 * 语义精炼： 利用基于语义距离的精炼，在生成合成训练样本之前增强“已选”与“拒绝”响应之间的对比。

在三个偏好数据集和两个奖励模型骨干网络上的实证结果表明，MARS始终优于标准的均匀增强、WoN以及AdaBoost风格的基线，从而实现更高的RewardBench得分和更高的对齐胜率。

MARS (Margin and Semantic-Aware Data Augmentation) is an adaptive framework designed to optimize reward modeling in low-resource environments. The framework introduces two key innovations: * Adaptive Allocation: It prioritizes augmentation for preference pairs with low margins, focusing computational resources where they are most needed. * Semantic Refinement: It utilizes semantic-distance-based refinement to enhance the contrast between "chosen" and "rejected" responses before generating synthetic training samples.

Empirical results across three preference datasets and two reward-model backbones demonstrate that MARS consistently outperforms standard uniform augmentation, WoN, and AdaBoost-style baselines, leading to improved RewardBench scores and higher alignment win rates.

Key Features

  • 受控增强： 根据偏好对的难度动态调整数据生成强度。
  • 增强对比： 通过精炼对之间的语义距离，提高合成数据的质量。
  • 稳健的性能： 通过下游对齐任务和独立裁判评估进行验证，确认性能提升是该方法固有的，而非模型耦合的产物。

Key Features

  • Controlled Augmentation: Dynamically adjusts the intensity of data generation based on the difficulty of the preference pair.
  • Enhanced Contrast: Improves the quality of synthetic data by refining the semantic distance between pairs.
  • Robust Performance: Validated through downstream alignment tasks and independent-judge evaluations, confirming that performance gains are intrinsic to the method rather than artifacts of model coupling.

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