Marginal-conditioned bridges enable training-free sampling from Flow Language Models by drawing clean one-hot endpoints from factorized posteriors and using Ornstein-Uhlenbeck bridges, preserving token marginals and reducing denoising error versus conditional-mean bridges.
Simple and effective masked diffusion language models
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cs.LG 2years
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S-FLM is a hyperspherical latent flow language model that learns velocity fields on the unit sphere to generate token sequences via deterministic ODE integration without materializing one-hot vectors.
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Sampling from Flow Language Models via Marginal-Conditioned Bridges
Marginal-conditioned bridges enable training-free sampling from Flow Language Models by drawing clean one-hot endpoints from factorized posteriors and using Ornstein-Uhlenbeck bridges, preserving token marginals and reducing denoising error versus conditional-mean bridges.
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Language Modeling with Hyperspherical Flows
S-FLM is a hyperspherical latent flow language model that learns velocity fields on the unit sphere to generate token sequences via deterministic ODE integration without materializing one-hot vectors.