The article discusses the differences between conventional models and reasoning models, likening them to Kahneman's System-1 and System-2 thinking. While LLMs like ChatGPT provide instant responses using large neural networks, they often lack the step-by-step reasoning ability required for more complex tasks. Recent advancements from companies like Anthropic aim to enhance reasoning capabilities through reinforcement learning and additional human data. Claude 3.7, for example, excels in coding tasks, demonstrating improvements in technical subjects that require in-depth reasoning, as highlighted by customer interest in practical applications of these models.
Anthropic says that Claude 3.7 is especially good at solving coding problems that require step-by-step reasoning, outscoring OpenAI's o1 on some benchmarks like SWE-bench.
The things that we made improvements on are [...] technical subjects or subjects which require long reasoning,
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