TL;DR
CopT introduces a novel reasoning approach that generates an initial answer before on-policy reflection, improving accuracy and efficiency in large language models without extra training.
Contribution
It reformulates reasoning as answer-first with on-policy reflection, using contrastive verifiers to assess answer reliability, enhancing performance and reducing token costs.
Findings
Improves peak accuracy by up to 23% across tasks.
Reduces token usage by up to 57%.
Operates without additional training.
Abstract
Chain-of-thought (CoT) is a standard approach for eliciting reasoning capabilities from large language models (LLMs). However, the common CoT paradigm treats thinking as a prerequisite for answering, which can delay access to plausible answers and incur unnecessary token costs even when the model is able to identify an answer before extended thinking, a behavior known as performative reasoning. In this paper, we introduce CopT, a reformulated reasoning pipeline that reverses the usual order of thinking and answering. Instead of thinking before answering, CopT first elicits a draft answer and then invokes subsequent on-policy thinking conditioned on its own draft answer for reflection and correction. To assess whether the draft answer should be trusted, CopT recasts continuous embeddings as inference-time contrastive verifiers. Specifically, it contrasts the model's support for the same…
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