AI Insight
This paper addresses "overthinking" in large reasoning models, where models continue reasoning redundantly after reaching correct solutions, wasting computational resources and sometimes reversing correct answers. The researchers developed ROM (Real-time Overthinking Mitigation), a lightweight detection system that monitors model hidden states to identify when productive reasoning transitions to redundant processing, then intervenes to stop generation at appropriate boundaries. Testing across five different model families and benchmarks showed ROM reduced response length by 28-77% while maintaining or improving accuracy in most cases, and decreased latency by 20-70%.
Why it matters
This approach could significantly reduce computational costs and energy consumption when deploying large reasoning models in practice, while maintaining or improving solution quality. The method is particularly relevant as AI reasoning systems scale up and computational efficiency becomes increasingly important for both economic and environmental reasons.
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⚠️ Preprint – Noch nicht peer-reviewed
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Abstract: Large reasoning models (LRMs) often suffer from overthinking, continuing with redundant verification and repeated attempts even after reaching a correct solution. This wastes computation and can overturn an initially correct answer. We show that the transition from productive to redundant reasoning is decodable from late-layer hidden states. These representations separate the two phases around first-correct-solution boundaries beyond what position and lexical controls explain, and distinguish correct from incorrect solution endings. Building on this finding, we propose ROM (Real-time Overthinking Mitigation), which uses a lightweight streaming detector (0.07–0.25% of backbone parameters) to monitor a frozen LRM and intervene at well-formed reasoning boundaries without answer extraction or probe decoding. Counterfactual Self-Correction (CSC) augments detector training with wrong$rightarrow$correct trajectories to preserve useful self-correction. Heads are trained separately for each backbone using shared MATH500 supervision and transfer zero-shot to four unseen benchmarks. Evaluated against ten baselines across five backbones from three families and five benchmarks, ROM$_{text{CSC}}$ reduces response length by 28–77% (45% on average), with significant reductions in all 25 settings and accuracy matching or exceeding vanilla decoding in 22. Measurements on two backbones further show 20–70% lower end-to-end latency with less than 0.5% detector overhead per decoding step. Code is available at https://github.com/SaFo-Lab/ROM.
Source: ROM: Real-time Overthinking Mitigation via Streaming Detection and Intervention