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Felix Fischer

FlipTip

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reacted to m-ric's post with 🔥 6 days ago

Introducing 𝗼𝗽𝗲𝗻 𝗗𝗲𝗲𝗽-𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 by Hugging Face! 💥 OpenAI's latest agentic app Deep Research seems really good... But it's closed, as usual. ⏱️ So with a team of cracked colleagues, we set ourselves a 24hours deadline to replicate and open-source Deep Research! ⏱️ ➡️ 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. ➡️ open Deep Research is at 55% (powered by o1), it is: - the best pass@1 solution submitted - the best open solution 💪💪 And it's only getting started ! Please jump in, drop PRs, and let's bring it to the top ! Read the blog post 👉 https://huggingface.co/blog/open-deep-research

reacted to m-ric's post with 🔥 6 days ago

Less is More for Reasoning (LIMO): a 32B model fine-tuned with 817 examples can beat o1-preview on math reasoning! 🤯 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 —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. ⚡ The Less-is-More Reasoning Hypothesis: ‣ Minimal but precise examples that showcase optimal reasoning patterns matter more than sheer quantity ‣ Pre-training knowledge plus sufficient computational resources at inference levels up math skills ➡️ Core techniques: ‣ High-quality reasoning chains with self-verification steps ‣ 817 handpicked problems that encourage deeper reasoning ‣ Enough inference-time computation to allow extended reasoning 💪 Efficiency gains: ‣ Only 817 examples instead of 100k+ ‣ 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 🚀 Read the full paper here 👉 https://huggingface.co/papers/2502.03387

reacted to m-ric's post with 👍 6 days ago

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reacted to m-ric's post with 🔥 6 days ago

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Introducing 𝗼𝗽𝗲𝗻 𝗗𝗲𝗲𝗽-𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 by Hugging Face! 💥

OpenAI's latest agentic app Deep Research seems really good... But it's closed, as usual.

⏱️ So with a team of cracked colleagues, we set ourselves a 24hours deadline to replicate and open-source Deep Research! ⏱️

➡️ 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.
➡️ open Deep Research is at 55% (powered by o1), it is:
- the best pass@1 solution submitted
- the best open solution 💪💪

And it's only getting started ! Please jump in, drop PRs, and let's bring it to the top !

Read the blog post 👉 https://huggingface.co/blog/open-deep-research

reacted to m-ric's post with 🔥👍 6 days ago

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2912

Less is More for Reasoning (LIMO): a 32B model fine-tuned with 817 examples can beat o1-preview on math reasoning! 🤯

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 —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.

⚡ The Less-is-More Reasoning Hypothesis:
‣ Minimal but precise examples that showcase optimal reasoning patterns matter more than sheer quantity
‣ Pre-training knowledge plus sufficient computational resources at inference levels up math skills

➡️ Core techniques:
‣ High-quality reasoning chains with self-verification steps
‣ 817 handpicked problems that encourage deeper reasoning
‣ Enough inference-time computation to allow extended reasoning

💪 Efficiency gains:
‣ Only 817 examples instead of 100k+
‣ 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 🚀

Read the full paper here 👉 LIMO: Less is More for Reasoning (2502.03387)