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Doula Isham Rashik Hasan

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AI & ML interests

Machine Learning, Deep Learning, Natural Language Processing

Recent Activity

reacted to m-ric's post with 🚀 13 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 🔥 13 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 👍 13 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
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Collections 2

Electrical Device Feedback Classification Models
The "Electrical Device Feedback Classification Models" collection contains models trained to classify customer feedback on electrical devices.
Electrical Engineering Named Entity Recognition (NER) Models
This collection features fine-tuned Named Entity Recognition (NER) models fine-tuned for the electrical engineering domain.