Composable Decoding on the Probability Simplex: Theory and Implementation

Abstract:Decoding for large language models is typically treated as a collection of isolated sampling strategies, with limited theoretical understanding of the behaviours they induce and how their underlying objectives relate. We formulate decoding as an optimisation problem over next-token distributions on the probability simplex, balancing expected model score against regularisation under support constraints. This view recovers familiar decoding methods through choices of regularisers and support constraints; more importantly, it enables new decoders to be constructed by composing distributional preferences within a single optimisation problem without external rewards, learned critics, or model parameter updates. We introduce CompoSimplex, a library with configurable support rules, regularisation primitives, and simplex solvers for constructing and evaluating compositional decoders. We evaluate standard samplers, individual regularisers, and compositions across multiple models and reasoning tasks. Our results show that compositions can realise trade-offs between single-sample quality, multi-sample quality, and diversity that are not attained by individual decoding objectives.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2609.34992 [cs.LG]

(or arXiv:2609.34992v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.34992 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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