Constraint-Aware Training

Abstract:When generating programs with language models, constrained decoding can apply program analyses to exclude tokens that violate syntax, scope, or typing rules. However, there is a duplication: standard training already teaches the model to suppress the tokens rejected by these analyses. This duplication leads to the question: if we will perform some analysis to filter a set tokens out during inference anyways, can we avoid teaching the model the said analysis altogether during training, and does this externalization lead to more efficient models? This paper defines a general constraint-aware objective satisfying this externalization desideratum and formalizes the benefits of externalization into three concrete theorems about model size and data efficiency. We show, through a controlled synthetic experiment, that the theorems survive training dynamics: constraint-aware training yields lower prediction loss at a matched parameter count and data compared to ordinary cross-entropy training, motivating training objectives that incorporate the analyses used during generation.

Machine Learning (cs.LG)

arXiv:2610.02909 [cs.LG]

(or arXiv:2610.02909v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.02909 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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