Big training projects appear to be including CoT reasoning traces in their training data

The article discusses the possibility that large language model training projects are incorporating Chain-of-Thought (CoT) reasoning traces into their training data, which could lead to improved reasoning capabilities.

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The article suggests that major AI training projects, such as those working on large language models, may be incorporating Chain-of-Thought (CoT) reasoning traces into their training data. CoT reasoning involves providing step-by-step logical explanations for how a problem is solved, rather than just the final answer. By including these reasoning traces, the training data could help the language models develop more robust and explainable reasoning capabilities. This could lead to significant improvements in the models' ability to solve complex problems and provide transparent, justifiable outputs.