Public articles linked to the same research event.
arXiv The work introduces Power-SMC, a training-free sampling method that maintains multiple candidate sequences in parallel, scores each with an importance weight measuring how well it matches the sequence-level power distribution, and periodically prunes low-scoring candidates in favor of high-scoring ones, matching or exceeding Metropolis-Hastings sampling in accuracy on MATH500, GSM8K, GPQA and HumanEval, preserving output diversity, and accelerating inference by up to 17.6x.
The work introduces Power-SMC, a training-free sampling method that maintains multiple candidate sequences in parallel, scores each with an importance weight measuring how well it matches the sequence-level power distribution, and periodically prunes low-scoring candidates in favor of high-scoring ones, matching or exceeding Metropolis-Hastings sampling in accuracy on MATH500, GSM8K, GPQA and HumanEval, preserving output diversity, and accelerating inference by up to 17.6x.
The work introduces Power-SMC, a training-free sampling method that maintains multiple candidate sequences in parallel, scores each with an importance weight measuring how well it matches the sequence-level power distribution, and periodically prunes low-scoring candidates in favor of high-scoring ones, matching or exceeding Metropolis-Hastings sampling in accuracy on MATH500, GSM8K, GPQA and HumanEval, preserving output diversity, and accelerating inference by up to 17.6x.
The work introduces Power-SMC, a training-free sampling method that maintains multiple candidate sequences in parallel, scores each with an importance weight measuring how well it matches the sequence-level power distribution, and periodically prunes low-scoring candidates in favor of high-scoring ones, matching or exceeding Metropolis-Hastings sampling in accuracy on MATH500, GSM8K, GPQA and HumanEval, preserving output diversity, and accelerating inference by up to 17.6x.