Large reasoning models have an overthinking problem. They reach the correct answer early in their chain of thought — then keep generating thousands of additional tokens reconsidering, double-checking, and exploring alternatives they'll ultimately discard. A new paper from researchers at UT Austin, EPFL, ENS Paris-Saclay, and Telecom Paris introduces TERMINATOR, an inference-time early-exit strategy that detects when a model has already generated its final answer and stops reasoning immediately.

The key insight is that the first arrival of a model's final answer in its chain of thought is detectable from hidden states. Token confidence spikes distinctly at the answer position. Thinking-word usage shifts — words like "hmm" and "okay" cluster before the answer; words like "another" and "alternatively" cluster after. These signals are real, consistent across math, coding, and science domains, and learnable by a small classifier.

TERMINATOR is a single transformer layer — initialized from the base model's final layer — with a binary prediction head trained to predict answer arrival at every token position. At inference time, a sliding window of the ten most recent predictions triggers a stop when majority vote says the answer is already there, injecting a close-thinking token into the token stream. No data-calibrated thresholds. No test-time distribution samples. Train once, deploy anywhere.

Tested on Qwen3-8B, Qwen3-14B, Ministral-3-8B-Reasoning, and Ministral-3-14B-Reasoning across MATH-500, AIME 2025, HumanEval, and GPQA:

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