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Felix Fischer

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reacted to m-ric's post with ๐Ÿ”ฅ about 1 month ago
Introducing ๐—ผ๐—ฝ๐—ฒ๐—ป ๐——๐—ฒ๐—ฒ๐—ฝ-๐—ฅ๐—ฒ๐˜€๐—ฒ๐—ฎ๐—ฟ๐—ฐ๐—ต by Hugging Face! ๐Ÿ’ฅ OpenAI's latest agentic app Deep Research seems really good... But it's closed, as usual. โฑ๏ธ So with a team of cracked colleagues, we set ourselves a 24hours deadline to replicate and open-source Deep Research! โฑ๏ธ โžก๏ธ We built open-Deep-Research, an entirely open agent that can: navigate the web autonomously, scroll and search through pages, download and manipulate files, run calculation on data... We aimed for the best performance: are the agent's answers really rigorous? On GAIA benchmark, Deep Research had 67% accuracy on the validation set. โžก๏ธ open Deep Research is at 55% (powered by o1), it is: - the best pass@1 solution submitted - the best open solution ๐Ÿ’ช๐Ÿ’ช And it's only getting started ! Please jump in, drop PRs, and let's bring it to the top ! Read the blog post ๐Ÿ‘‰ https://huggingface.co/blog/open-deep-research
reacted to m-ric's post with ๐Ÿ”ฅ about 1 month ago
Less is More for Reasoning (LIMO): a 32B model fine-tuned with 817 examples can beat o1-preview on math reasoning! ๐Ÿคฏ Do we really need o1's huge RL procedure to see reasoning emerge? It seems not. Researchers from Shanghai Jiaotong University just demonstrated that carefully selected examples can boost math performance in large language models using SFT โ€”no huge datasets or RL procedures needed. Their procedure allows Qwen2.5-32B-Instruct to jump from 6.5% to 57% on AIME and from 59% to 95% on MATH, while using only 1% of the data in previous approaches. โšก The Less-is-More Reasoning Hypothesis: โ€ฃ Minimal but precise examples that showcase optimal reasoning patterns matter more than sheer quantity โ€ฃ Pre-training knowledge plus sufficient computational resources at inference levels up math skills โžก๏ธ Core techniques: โ€ฃ High-quality reasoning chains with self-verification steps โ€ฃ 817 handpicked problems that encourage deeper reasoning โ€ฃ Enough inference-time computation to allow extended reasoning ๐Ÿ’ช Efficiency gains: โ€ฃ Only 817 examples instead of 100k+ โ€ฃ 40.5% absolute improvement across 10 diverse benchmarks, outperforming models trained on 100x more data This really challenges the notion that SFT leads to memorization rather than generalization! And opens up reasoning to GPU-poor researchers ๐Ÿš€ Read the full paper here ๐Ÿ‘‰ย https://huggingface.co/papers/2502.03387
reacted to m-ric's post with ๐Ÿ‘ about 1 month ago
Less is More for Reasoning (LIMO): a 32B model fine-tuned with 817 examples can beat o1-preview on math reasoning! ๐Ÿคฏ Do we really need o1's huge RL procedure to see reasoning emerge? It seems not. Researchers from Shanghai Jiaotong University just demonstrated that carefully selected examples can boost math performance in large language models using SFT โ€”no huge datasets or RL procedures needed. Their procedure allows Qwen2.5-32B-Instruct to jump from 6.5% to 57% on AIME and from 59% to 95% on MATH, while using only 1% of the data in previous approaches. โšก The Less-is-More Reasoning Hypothesis: โ€ฃ Minimal but precise examples that showcase optimal reasoning patterns matter more than sheer quantity โ€ฃ Pre-training knowledge plus sufficient computational resources at inference levels up math skills โžก๏ธ Core techniques: โ€ฃ High-quality reasoning chains with self-verification steps โ€ฃ 817 handpicked problems that encourage deeper reasoning โ€ฃ Enough inference-time computation to allow extended reasoning ๐Ÿ’ช Efficiency gains: โ€ฃ Only 817 examples instead of 100k+ โ€ฃ 40.5% absolute improvement across 10 diverse benchmarks, outperforming models trained on 100x more data This really challenges the notion that SFT leads to memorization rather than generalization! And opens up reasoning to GPU-poor researchers ๐Ÿš€ Read the full paper here ๐Ÿ‘‰ย https://huggingface.co/papers/2502.03387
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