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Chopthin-Consensus Power Sampling (CCPS) is a diversity-preserving method for LLM decoding that improves reasoning accuracy without post-training by preserving distinct reasoning paths and using semantic-majority selection, as demonstrated in benchmarks.
This paper introduces Entropy-Guided Power Sampling (EGPS), a training-free and verifier-free sampler that improves the efficiency of power sampling for enhancing base language model reasoning. EGPS achieves up to 12.6x speedup over standard Metropolis-Hastings sampling while reaching best or tied-best accuracy on benchmarks like MATH500, HumanEval, and GPQA.