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๐—”๐—ฑ๐˜†๐—ฒ๐—ป'๐˜€ ๐—ป๐—ฒ๐˜„ ๐——๐—ฎ๐˜๐—ฎ ๐—”๐—ด๐—ฒ๐—ป๐˜๐˜€ ๐—•๐—ฒ๐—ป๐—ฐ๐—ต๐—บ๐—ฎ๐—ฟ๐—ธ ๐˜€๐—ต๐—ผ๐˜„๐˜€ ๐˜๐—ต๐—ฎ๐˜ ๐——๐—ฒ๐—ฒ๐—ฝ๐—ฆ๐—ฒ๐—ฒ๐—ธ-๐—ฅ๐Ÿญ ๐˜€๐˜๐—ฟ๐˜‚๐—ด๐—ด๐—น๐—ฒ๐˜€ ๐—ผ๐—ป ๐—ฑ๐—ฎ๐˜๐—ฎ ๐˜€๐—ฐ๐—ถ๐—ฒ๐—ป๐—ฐ๐—ฒ ๐˜๐—ฎ๐˜€๐—ธ๐˜€! โŒ โžก๏ธ How well do reasoning models perform on agentic tasks? Until now, all indicators seemed to show that they worked really well. On our recent reproduction of Deep Search, OpenAI's o1 was by far the best model to power an agentic system. So when our partner Adyen built a huge benchmark of 450 data science tasks, and built data agents with smolagents to test different models, I expected reasoning models like o1 or DeepSeek-R1 to destroy the tasks at hand. ๐Ÿ‘Ž But they really missed the mark. DeepSeek-R1 only got 1 or 2 out of 10 questions correct. Similarly, o1 was only at ~13% correct answers. ๐Ÿง These results really surprised us. We thoroughly checked them, we even thought our APIs for DeepSeek were broken and colleagues Leandro Anton helped me start custom instances of R1 on our own H100s to make sure it worked well. But there seemed to be no mistake. Reasoning LLMs actually did not seem that smart. Often, these models made basic mistakes, like forgetting the content of a folder that they had just explored, misspelling file names, or hallucinating data. Even though they do great at exploring webpages through several steps, the same level of multi-step planning seemed much harder to achieve when reasoning over files and data. It seems like there's still lots of work to do in the Agents x Data space. Congrats to Adyen for this great benchmark, looking forward to see people proposing better agents! ๐Ÿš€ Read more in the blog post ๐Ÿ‘‰ https://huggingface.co/blog/dabstep
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