AI Research
Chopthin-Consensus Power Sampling: A Diversity-Preserving Approach to LLM Decoding
Improving LLM reasoning through robust diversity-preserving trajectory management.
Inference-time power sampling via Sequential Monte Carlo (SMC) can substantially improve large language model (LLM) reasoning without requiring post-training. However, many existing SMC approaches rely on equal-weight resampling, which can aggressively prune low-weight trajectori...
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ArXiv CS.CL
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ArXiv CS.CL
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Chopthin-consensus sampling represents a major leap in reasoning reliability by preserving diverse solution paths during the decoding process. This method allows LLMs to avoid the narrow traps of greedy search, unlocking higher accuracy without the heavy overhead of additional fine-tuning.
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