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arXiv AI By Hugh Van Deventer, Dmitrii Troitskii, Laura Ruis

The First Few Tokens Matter: Understanding Chain-of-Thought Controllability

Read the original on arXiv AI →

The paper investigates why chain-of-thought (CoT) reasoning in language models is hard to control, attributing the issue to an entropy collapse in the first few tokens after the opening thinking tag. By applying two inference-time interventions—prefilling a single token and banning frequent opening tokens with in-context examples—the authors significantly improve CoT controllability, especially for addition and style tasks. The study also distinguishes between addition/style and suppression controllability, revealing different underlying mechanisms and correlations with monitorability across models.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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