ROM: Real-time Overthinking Mitigation for Large Reasoning Models
Researchers have introduced ROM, a model-agnostic framework designed to mitigate overthinking in Large Reasoning Models (LRMs). LRMs often continue generating redundant reasoning steps after reaching a correct solution, wasting computational resources and potentially overturning accurate answers. ROM addresses this by detecting the transition from productive to redundant reasoning through hidden-state analysis at the first-correct-solution boundary. The framework employs a lightweight detector to monitor frozen LRMs and intervenes using Counterfactual Self-Correction to distinguish useful corrections from unnecessary post-solution exploration. Tested on benchmarks like MATH500 and GSM8K, ROM improved accuracy and significantly reduced response lengths for both Qwen3-8B and DeepSeek-R1-Distill-Qwen-32B models. For instance, it reduced token usage by approximately 27% for Qwen3-8B while slightly boosting accuracy. The method also decreased wall-clock latency by 46.5% and proved compatible with additional compression techniques. These findings suggest a shared structural boundary in long Chain-of-Thought processes across different model scales, offering a scalable solution for efficient AI reasoning.
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ROM: Real-time Overthinking Mitigation for Large Reasoning Models
Researchers have introduced ROM, a model-agnostic framework designed to mitigate overthinking in Large Reasoning Models (LRMs). LRMs often continue generating redundant reasoning steps after reaching a correct solution, wasting computational resources and potentially overturning accurate answers. ROM addresses this by detecting the transition from productive to redundant reasoning through hidden-state analysis at the first-correct-solution boundary. The framework employs a lightweight detector to monitor frozen LRMs and intervenes using Counterfactual Self-Correction to distinguish useful corrections from unnecessary post-solution exploration. Tested on benchmarks like MATH500 and GSM8K, ROM improved accuracy and significantly reduced response lengths for both Qwen3-8B and DeepSeek-R1-Distill-Qwen-32B models. For instance, it reduced token usage by approximately 27% for Qwen3-8B while slightly boosting accuracy. The method also decreased wall-clock latency by 46.5% and proved compatible with additional compression techniques. These findings suggest a shared structural boundary in long Chain-of-Thought processes across different model scales, offering a scalable solution for efficient AI reasoning.
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