S pec C o T : Accelerating Chain-of-Thought Reasoning through Speculative Exploration (2025)

Abstract: Large Reasoning Models (LRMs) demonstrate strong performance on complex tasks through chain-of-thought (CoT) reasoning. However, they suffer from high inference latency due to lengthy reasoning chains. In this paper, we propose SpecCoT, a collaborative framework that combines large and small models for effective yet efficient reasoning. Unlike traditional speculative decoding, which operates at the token level, SpecCoT adopts a step-level verification strategy: the large model first establishes the reasoning direction, and for each intermediate step, the small model generates multiple candidate drafts in parallel. The large model then verifies these drafts, either selecting the most suitable one or rejecting them all and generating its own. SpecCoT approach balances reasoning quality with inference efficiency through fine-grained model cooperation. Experiments across diverse tasks show SpecCoT reduces inference latency by 1.7-4.1 \times while maintaining comparable accuracy to standard large model inference.

## introduction recent advances in large reasoning models (lrms) @xcite @xcite have transformed ai reasoning through chain-of-thought (cot) @xcite ) mechanisms. by decomposing complex problems into intermediate steps, these models achieve stateof-the-art performance across various domains. unlike traditional large language models, lrms first generate and refine step-by-step reasoning before producing conclusions. however, this process incurs substantial computational costs-lengthy reasoning chains combined with billions of parameters result in inference latency ranging from minutes to hours, rendering these powerful systems impractical for time-sensitive applications. current approaches address this challenge by either developing smaller reasoning models through distillation techniques @xcite or reducing cot generation length using methods like stepskip @xcite and tokenskip @xcite . however, these approaches face a fundamental limitation when reasoning models encounter problems of varying complexity: large models are computationally inefficient for simple tasks, while smaller models or compressed reasoning chains often fail to handle complex problems that require detailed step-by-step deduction. this critical trade-off between computational efficiency and reasoning thoroughness presents a key open problem and motivates further exploration of adaptive reasoning frameworks. @xcite @xcite . the success of speculative decoding @xcite @xcite illustrates how collaboration between large and small models can effectively reduce inference latency. in this paradigm, a small model rapidly drafts potential continuations while a large model verifies their quality, achieving significant speedup in text generation @xcite . this collaborative approach is particularly well-suited for chain-ofthought reasoning due to its structural characteristics-unlike standard text generation, where tokens contribute equally, reasoning chains comprise distinct steps of varying complexity and importance, from crucial logical deductions to straightforward elaborations. this inherent structure suggests an opportunity to enhance the speculative paradigm by leveraging semantic-level verification rather than requiring exact token matches. in this paper, we propose speccot (speculative chain-of-thought), a collaborative reasoning framework that improves efficiency by combining small-model draft generation with large-model step-level verification. instead of compressing models or simplifying reasoning chains, speccot employs a strategic multi-phase process: initially, a large model generates the opening tokens to establish the correct reasoning trajectory; subsequently, for each intermediate step, a lightweight small model efficiently produces multiple candidate continuations in parallel. the large model then evaluates these drafts simultaneously, either accepting the most suitable candidate or intervening to generate the step itself when none are acceptable. this design preserves reasoning accuracy while improving efficiency by delegating intermediate step generation to the small model and reserving the large model for only validation and critical reasoning. we evaluate speccot on diverse reasoning tasks spanning different complexity levels. our experiments demonstrate that speccot reduces inference latency by up to 4.1× compared to standard large model inference while maintaining comparable reasoning accuracy. the efficiency gains are particularly significant for complex mathematical problems requiring long reasoning chains, where our method effectively balances computational cost and reasoning quality. our key contributions are: ## motivation complex reasoning tasks present unique challenges and opportunities for language models of different sizes. in this section, we explore the inherent tradeoffs between models of varying scales and identify key opportunities for optimization. ## method we propose speculative chain-of-thought (spec-cot), a collaborative reasoning framework that leverages the complementary strengths of large and small language models. the key insight of our approach is to decompose the chain-of-thought reasoning process into speculative iterations, where a large model provides initial guidance and performs efficient verification, while a small model generates multiple diverse intermediate reasoning steps throughout the process. this collaborative approach enables us to maintain the reasoning quality and reflective capabilities of large models while achieving significant computational efficiency improvements through parallel candidate generation. ## conclusion in this paper, we presented speccot, demonstrating that efficient chain-of-thought reasoning can be achieved through strategic collaboration between large and small models. our approach achieves 1.7-4.1× reduction in reasoning time while maintaining strong reasoning capabilities across various benchmarks.