Chain-of-thought reasoning in large models contains a sharp 'commitment boundary' where the answer solidifies—you can safely stop reasoning early and save 55% compute without losing performance.
This paper reveals that large language models often decide their final answer early in chain-of-thought reasoning, with many subsequent steps having no causal effect on the result. By measuring when models commit to an answer, researchers show this happens in a single step on average, followed by 'epiphenomenal' reasoning that doesn't change the outcome.