Haoxing Zhai

MuTIS: Enhancing Reasoning Efficiency through Multi Turn Intervention Sampling in Reinforcement Learning

Recently, large reasoning models (LRMs) have demonstrated state-of-the-art performance across a wide range of benchmarks. However, a common challenge for these models is the “overthinking” problem, which leads to excessive reasoning steps and significant computational overhead. Furthermore, the issu