TL;DR
ROM is a streaming detection and intervention framework that reduces overthinking in large reasoning models, improving efficiency and accuracy by monitoring hidden states and intervening at reasoning boundaries.
Contribution
It introduces a model-agnostic, boundary-based intervention method that leverages hidden states to mitigate redundant reasoning in large models, with cross-scale transferability.
Findings
ROM improves accuracy on multiple benchmarks with reduced token usage.
It transfers supervision across different model scales and training origins.
ROM reduces latency and token count without accuracy loss.
Abstract
Large Reasoning Models (LRMs) often reach a correct solution before their long Chain-of-Thought trace ends, yet continue with redundant verification, repeated attempts, or unnecessary exploration that wastes computation and can even overturn the correct answer. We frame this behavior as a latent productive-to-redundant transition and show that it is directly reflected in hidden states: around first-correct-solution (FCS) boundaries, late-layer representations separate efficient from overthinking tokens, while boundary-permutation and position-control baselines collapse. Based on this signal, we propose ROM, a model-agnostic streaming intervention framework that monitors frozen LRMs with a lightweight hidden-state detector and intervenes at well-formed reasoning boundaries. Counterfactual Self-Correction (CSC) augments supervision with balanced wrong to correct trajectories, preserving…
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