Reasoning Relay: Evaluating Stability and Interchangeability of Large Language Models in Mathematical Reasoning
Analysis
This ArXiv paper explores the interchangeability of reasoning chains between different large language models (LLMs) during mathematical problem-solving. The core question is whether a partially completed reasoning process from one model can be reliably continued by another, even across different model families. The study uses token-level log-probability thresholds to truncate reasoning chains at various stages and then tests continuation with other models. The evaluation pipeline incorporates a Process Reward Model (PRM) to assess logical coherence and accuracy. The findings suggest that hybrid reasoning chains can maintain or even improve performance, indicating a degree of interchangeability and robustness in LLM reasoning processes. This research has implications for understanding the trustworthiness and reliability of LLMs in complex reasoning tasks.
Key Takeaways
- •LLMs can potentially interchange reasoning steps during complex tasks.
- •Hybrid reasoning chains may improve accuracy and logical structure.
- •Process Reward Models (PRMs) offer a framework for evaluating reasoning stability.
“Evaluations with a PRM reveal that hybrid reasoning chains often preserve, and in some cases even improve, final accuracy and logical structure.”