Efficient Reasoning for LLMs through Speculative Chain-of-Thought (2504.19095v2)

Abstract: Large reasoning LLMs such as OpenAI-o1 and Deepseek-R1 have recently attracted widespread attention due to their impressive task-solving abilities. However, the enormous model size and the generation of lengthy thought chains introduce significant reasoning costs and response latency. Existing methods for efficient reasoning mainly focus on reducing the number of model parameters or shortening the chain-of-thought length. In this paper, we introduce Speculative Chain-of-Thought (SCoT), which reduces reasoning latency from another perspective by accelerated average reasoning speed through large and small model collaboration. SCoT conducts thought-level drafting using a lightweight draft model. Then it selects the best CoT draft and corrects the error cases with the target model. The proposed thinking behavior alignment improves the efficiency of drafting and the draft selection strategy maintains the prediction accuracy of the target model for complex tasks. Experimental results on GSM8K, MATH, GaoKao, CollegeMath and Olympiad datasets show that SCoT reduces reasoning latency by 48\%$\sim$66\% and 21\%$\sim$49\% for Deepseek-R1-Distill-Qwen-32B and Deepseek-R1-Distill-Llama-70B while achieving near-target-model-level performance. Our code is available at https://github.com/Jikai0Wang/Speculative_CoT.

Efficient Reasoning for LLMs through Speculative Chain-of-Thought

This paper presents a novel framework named Speculative Chain-of-Thought (SCoT) that enhances the efficiency of reasoning in LLMs by integrating both large and small models for collaborative reasoning tasks. The research addresses the computational and latency challenges often encountered with large-scale models like Deepseek-R1-Distill-Qwen-32B, especially when generating extensive chain-of-thought (CoT) sequences.

SCoT proposes a dual-model strategy wherein a smaller model initially performs thought-level drafting. This smaller model is faster and provides multiple drafts for any given reasoning problem. Subsequently, these drafts are evaluated, and the most suitable one is selected by the larger target model, which also corrects potential errors. This approach diverges from traditional methods that typically focus on either reducing model parameters or shortening the CoT length.

Key Findings

The paper provides empirical evidence of SCoT's efficacy through evaluations on several datasets, including GSM8K, MATH, GaoKao, CollegeMath, and Olympiad. Notably, implementing SCoT reduces reasoning latency by 48% to 66%, while maintaining accuracy close to that of traditional large models across these datasets. Furthermore, the analysis indicates that SCoT achieves an average speed-up ratio of up to 2.92× in reasoning tasks. This noted reduction in latency and maintenance of accuracy highlight the method's utility.

Methodology

The framework introduces several novel features to achieve its results:

1. Thought-Level Speculation:
   • The process involves speculative generation of thought chains using a smaller, faster model. Unlike token-level speculative decoding, thought-level speculation accelerates reasoning by preparing multiple thought sequences concurrently.
2. Thinking Behavior Alignment:
   • The draft model’s efficiency in generating meaningful drafts is enhanced by aligning its thinking behaviors with that of the target model using fine-tuning via LoRA modules. This step ensures that drafts are more coherent and have reduced redundancy, as evidenced by the shrinkage in average CoT length.
3. Draft Selection and Error Correction:
   • The target model, fine-tuned similarly, performs draft selection. A special draft option is added for cases where none of the generated drafts is correct, invoking the target model to reconsider the problem solution to maintain accuracy.

Implications and Future Directions

The implications of SCoT are twofold. Practically, it offers a scalable approach to leveraging existing LLM infrastructures without compromising on performance for computational efficiency. Theoretically, it suggests alternative pathways to model design, whereby collaborative systems draw on the strengths of various model sizes.

For future research, several paths are worth exploring. One could investigate additional fine-tuning strategies or improvements in speculative decoding to further refine the accuracy and efficiency trade-offs. Expanding the framework's applicability to other domains or task types could also bring about advances in adaptive reasoning techniques. Additionally, how speculative thought chains might interact with emergent tasks not covered in typical datasets may represent another frontier.

Overall, this research presents a compelling advancement in optimizing the operational costs of LLMs while preserving high-level reasoning capabilities, thereby setting a foundation for widespread and practical applications of artificial intelligence in complex reasoning tasks.