Exploring Solution Divergence and Its Effect on Large Language Model Problem Solving

A study published in September 2026 proposed “solution space divergence” as a new metric to measure the problem-solving ability of large language models, and found that this metric was positively correlated with model performance. The study was conducted on three representative problem domains and confirmed that strategies based on this metric could continuously improve the success rate of large language models in solving problems. This finding indicates that solution space divergence can serve as an effective tool to support supervised fine-tuning (SFT) and reinforcement learning (RL), and can contribute to the development of training and evaluation methods for large language models.