Efficient reasoning training does not always harm chain-of-thought faithfulness and monitorability, according to research addressing the concern that training large language models to solve tasks with fewer tokens degrades the inspectability of their reasoning. Chain-of-thought reasoning lets humans inspect how large language models reach their answers and oversee model behaviour, but it increases inference cost, motivating efficient methods that use fewer tokens.
Chain-of-thought (CoT) reasoning allows humans to inspect how large language models reach their answers, and oversee model behaviour. This reasoning comes at an increased inference cost, motivating efficient methods that train models to solve tasks using fewer tokens. However, a common concern is th
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