NI Sampling accelerates discrete diffusion language models up to 14.3 times by training a neural indicator to select which tokens to sample at each step using a trajectory-preserving objective.
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
ME reinterprets LLM ensembling as a mixture model by sampling a single model stochastically at each token step, matching the ensemble distribution while invoking only one model per step for substantial speed gains.
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NI Sampling: Accelerating Discrete Diffusion Sampling by Token Order Optimization
NI Sampling accelerates discrete diffusion language models up to 14.3 times by training a neural indicator to select which tokens to sample at each step using a trajectory-preserving objective.
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Rethinking LLM Ensembling from the Perspective of Mixture Models
ME reinterprets LLM ensembling as a mixture model by sampling a single model stochastically at each token step, matching the ensemble distribution while invoking only one model per step for substantial speed gains.