TERMINATOR: Learning Optimal Exit Points for Early Stopping in Chain-of-Thought Reasoning
Large Reasoning Models (LRMs) achieve impressive performance on complex reasoning tasks via Chain-of-Thought (CoT) reasoning, which enables them to generate intermediate thinking tokens before arriving at the final answer. However, LRMs often suffer from significant overthinking, spending excessive compute time even after the answer is generated early on. Prior work has identified the existence of an optimal reasoning length such that truncating reasoning at this point significantly shortens CoT outputs with virtually no change in performance. However, determining optimal CoT lengths for practical datasets is highly non-trivial as they are fully task and model-dependent. In this paper, we...
| Authors | Alliot Nagle, Jakhongir Saydaliev, Dhia Garbaya et al. |
|---|---|
| Primary category | cs.LG |
| Published | 13 March 2026 |
| Last updated at source | 14 May 2026 |
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