Does Verbose Chain-of-Thought Really Help? In-Distribution Evidence that Content, Not Length, Matters
arXiv:2606.30128v1 Announce Type: new Abstract: Chain-of-thought (CoT) prompting improves LLM reasoning, but the source is contested: do the intermediate steps help because they carry useful semantic content, or because conditioning on more tokens buys extra computation before the model commits to an answer? We bring two lines of evidence to bear. First, in distribution: we repeatedly sample each model on the same question and pair a shorter with a longer of its own natural generations that follow the same reasoning plan, so nothing is rewritten and both traces are genuinely in-distribution. A
The proliferation of Chain-of-Thought (CoT) prompting in LLMs necessitates a deeper understanding of its efficacy beyond anecdotal observations, especially as compute resources become a bottleneck.
This research provides empirical evidence that the quality and content of reasoning steps, rather than mere verbosity, are critical for LLM performance, impacting future research and development in AI.
The focus for improving LLM reasoning will shift further towards generating meaningful intermediate steps rather than simply extending computation through longer prompts.
- · AI researchers focusing on semantic reasoning
- · Developers of more efficient LLM prompting techniques
- · Companies optimizing LLM compute expenditure
- · Those relying solely on prompt length for performance gains
- · LLM architectures that cannot capture complex reasoning content
Further research into the cognitive processes within LLMs will be spurred by the distinction between content and length in CoT prompting.
The development of LLMs will prioritize generating concise, high-quality reasoning steps, potentially leading to more interpretable and efficient models.
This could contribute to the broader availability and lower cost of advanced AI capabilities as models become more compute-efficient through better prompting.
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Read at arXiv cs.AI