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Felix Fischer

FlipTip

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reacted to m-ric's post with ๐Ÿ”ฅ about 20 hours 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 20 hours 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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FlipTip's activity

New activity in Nexusflow/Athene-70B 7 months ago

Training Data?

#8 opened 7 months ago by
FlipTip
New activity in LiteLLMs/Phi-3-small-8k-instruct-GGUF 7 months ago

๐Ÿšฉ Report: Spam

#3 opened 7 months ago by
FlipTip
New activity in huggingchat/chat-ui 9 months ago

[TOOLS] Community Discussion

27
#455 opened 9 months ago by
victor
New activity in huggingchat/chat-ui 10 months ago

Add vision capabilities.

#443 opened 10 months ago by
FlipTip

Llama 3 120B?

1
#439 opened 10 months ago by
Tommy84
New activity in google/sdxl about 1 year ago
New activity in stabilityai/stable-diffusion about 2 years ago