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When AI reasoning goes wrong: Microsoft Research shows more tokens can mean more problems
Not all AI scaling strategies are equal. Longer reasoning chains are not sign of higher intelligence. More compute isn't always the answer.
Large language models (LLMs) are increasingly capable of complex reasoning through “ inference-time scaling,” a set of techniques that allocate more computational resources during inference to generate answers. Another important finding is the consistent performance boost from perfect verifiers, which highlights a critical area for future work: building robust and broadly applicable verification mechanisms. “The necessity of connecting the two comes from the fact that users will not always formulate their queries in a formal way, they will want to use a natural language interface and expect the solutions in a similar format or in a final action (e.g. propose a meeting invite).”
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