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add results table

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@@ -9,6 +9,19 @@ INTELLECT-MATH is a 7B parameter model optimized for mathematical reasoning. It
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  We demonstrate that the quality of our SFT data can impact the performance and training speed of the RL stage: Due to its better synthetic SFT dataset that encourages the model to imitate the reasoning behavior of a strong teacher model, INTELLECT-MATH outperforms Eurus-2-PRIME, the previous state-of-the-art trained with PRIME-RL, and matches its performance with 10x faster training.
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  ### Links
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  - 📜 [Blog Post]()
 
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  We demonstrate that the quality of our SFT data can impact the performance and training speed of the RL stage: Due to its better synthetic SFT dataset that encourages the model to imitate the reasoning behavior of a strong teacher model, INTELLECT-MATH outperforms Eurus-2-PRIME, the previous state-of-the-art trained with PRIME-RL, and matches its performance with 10x faster training.
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+ | | Intellect-Math (Step 255) | Intellect-Math (Step 47) | Eurus-2-Prime (Step 592) | Intellect-Math-SFT | Eurus-2-SFT | Qwen-2.5-Math |
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+ |----------------|---------------------------:|--------------------------:|--------------------------:|--------------------:|------------:|-------------:|
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+ | **MATH-500** | 82.0 | 81.6 | 79.2 | 72.8 | 65.1 | 79.8 |
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+ | **OLYMPIADBENCH** | 49.5 | 46.7 | 42.1 | 39.1 | 29.8 | 40.7 |
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+ | **AIME 2024** | 26.7 | 26.7 | 26.7 | 16.6 | 3.3 | 13.3 |
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+ | **AMC** | 60.2 | 57.8 | 57.8 | 45.8 | 30.1 | 50.6 |
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+ | **MINERVA MATH** | 39.7 | 37.8 | 38.6 | 33.8 | 32.7 | 34.6 |
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+ | **AVG** | 51.6 | 50.1 | 48.9 | 41.6 | 32.2 | 43.8 |
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  ### Links
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  - 📜 [Blog Post]()