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So-called reasoning models are more efficient but not more capable than regular LLMs, study finds
So-called reasoning models are more efficient but not more capable than regular LLMs, study finds

A new study from Tsinghua University and Shanghai Jiao Tong University examines whether reinforcement learning with verifiable rewards (RLVR) helps large language models reason better—or simply makes them more efficient at repeating known solutions.
The article So-called reasoning models are more efficient but not more capable than regular LLMs, study finds appeared first on THE DECODER.

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