Abstract
As large language models expand beyond natural language to domains such as mathematics, multimodal understanding, and embodied agents, tokens increasingly reflect metric relationships rather than purely linguistic meaning. We introduce DIST2Loss, a distance-aware framework designed to train autoregressive discrete models by leveraging predefined distance relationships among output tokens. At its core, DIST2Loss transforms continuous exponential family distributions derived from inherent distance metrics into discrete, categorical optimization targets compatible with the models' architectures. This approach enables the models to learn and preserve meaningful distance relationships during token generation while maintaining compatibility with existing architectures. Empirical evaluations show consistent performance gains in diverse multimodal applications, including visual grounding, robotic manipulation, generative reward modeling, and image generation using vector-quantized features. These improvements are most notable in low-data regimes, demonstrating DIST2Loss's strength under resource constraints.
Abstract (translated)
随着大型语言模型从自然语言扩展到数学、多模态理解和具身代理等领域，标记（tokens）越来越多地反映出度量关系而非纯粹的语言意义。我们引入了DIST2Loss，这是一种距离感知框架，旨在通过利用输出标记之间预定义的距离关系来训练自回归离散模型。在核心部分，DIST2Loss将从固有距离度量中导出的连续指数族分布转换为与模型架构兼容的离散分类优化目标。这一方法使模型能够在生成令牌时学习和保留有意义的距离关系，并且同时保持与现有架构的兼容性。 实验评估显示，在包括视觉基础、机器人操作、生成奖励建模以及使用向量量化特征进行图像生成在内的多种多模态应用中，性能均有持续提升。在低数据环境下的改进尤其显著，这表明DIST2Loss在资源受限的情况下具有强大的能力。
URL
https://arxiv.org/abs/2503.02379
