Title: Teaching Metric Distance to Discrete Autoregressive Language Models
Primary area: foundation or frontier models, including LLMs
Keywords: Large Language Models, Autoregressive Modeling, Generative Modeling, Efficient Training

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.

TL;DR: DIST2Loss trains discrete models to respect token distances, boosting performance in diverse domains, especially with limited data.