Count-FM is a new flow-matching method for count data based on birth-death processes that achieves better sample quality with fewer parameters than baselines on simulations and real scRNA-seq and spike-train data.
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DPRM introduces a Doob h-transform process reward module as a plug-in for token ordering in diffusion language models, with convergence proofs and empirical gains over confidence baselines especially on hard reasoning and scientific design tasks.
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Flow Matching for Count Data
Count-FM is a new flow-matching method for count data based on birth-death processes that achieves better sample quality with fewer parameters than baselines on simulations and real scRNA-seq and spike-train data.
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DPRM: A Plug-in Doob h transform-induced Token-Ordering Module for Diffusion Language Models
DPRM introduces a Doob h-transform process reward module as a plug-in for token ordering in diffusion language models, with convergence proofs and empirical gains over confidence baselines especially on hard reasoning and scientific design tasks.