Unverified50% confidenceFactExact time
DoubtLess is a training-free contrastive decoding method that uses a small reasoning model to guide a large model to generate more concise chain-of-thought without loss of accuracy.
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10/14/2026
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VerifiedSpeculative decoding is a lossless acceleration technique that does not alter the model's output distribution; it uses a draft model with far fewer parameters than the target model to generate candidate tokens, which the target model then verifies in parallel.77% similarUnverifiedThe core mechanism of DoubtLess is: let an auxiliary small model read the CoT already generated by the large model under two prompts, concise and verbose, respectively, then multiply the difference between the logit vectors under the two prompts by an intensity coefficient and add it to the logits of the main model's decoding at each step.75% similarUnverified若训练集能充分覆盖推理时可能出现的工具类型和参数组合，小模型同样能学到可靠的格式生成能力，而不必依赖大模型的深层语义推理72% similarUnverifiedThe systematic process for debugging a non-converging model is to first verify on a very small data subset whether the model can overfit; if it cannot overfit even on small data, it indicates a bug in the model or training code.72% similar
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