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@@ -32,7 +32,7 @@ Nemotron-Math-Proofs-v3-SFT is a long-form mathematical reasoning dataset contai
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  The source pool contains 15,879 hard proof problems selected from the [AoPS](https://artofproblemsolving.com/) subset of [nvidia/Nemotron-Math-Proofs-v1](https://huggingface.co/datasets/nvidia/Nemotron-Math-Proofs-v1). Responses are generated using [DeepSeek-V4-Pro](https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro) in Max inference mode. A four-round generate-verify-refine pipeline produces initial proofs in round 1 and refinement trajectories for problems that remain unsolved in rounds 2 through 4. The pipeline also produces verifier and meta-verifier traces following the proof-generation and proof-verification prompting style described in the [DeepSeekMath-V2 paper](https://arxiv.org/abs/2511.22570).
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- Full details about dataset construction can be found in our technical report [An Open Recipe for IMO Gold: Training Nemotron for Olympiad Mathematics](https://github.com/NVIDIA-NeMo/Skills/blob/main/recipes/nemotron-imo-tts/paper.pdf).
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  This dataset is ready for commercial or non-commercial uses.
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@@ -141,7 +141,7 @@ Total Recorded Token Count: 39,047,191,715
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  Generated token counts are taken from the inference metadata. Input token counts are pipeline estimates based on prompt text length rather than a tokenizer re-count.
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  ## Reference(s):
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- * [An Open Recipe for IMO Gold: Training Nemotron for Olympiad Mathematics](https://github.com/NVIDIA-NeMo/Skills/blob/main/recipes/nemotron-imo-tts/paper.pdf) (technical report)
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  * [DeepSeek-V4-Pro](https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro)
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  * [nvidia/Nemotron-Math-Proofs-v1](https://huggingface.co/datasets/nvidia/Nemotron-Math-Proofs-v1)
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  * [nvidia/Nemotron-Math-Proofs-v2](https://huggingface.co/datasets/nvidia/Nemotron-Math-Proofs-v2)
 
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  The source pool contains 15,879 hard proof problems selected from the [AoPS](https://artofproblemsolving.com/) subset of [nvidia/Nemotron-Math-Proofs-v1](https://huggingface.co/datasets/nvidia/Nemotron-Math-Proofs-v1). Responses are generated using [DeepSeek-V4-Pro](https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro) in Max inference mode. A four-round generate-verify-refine pipeline produces initial proofs in round 1 and refinement trajectories for problems that remain unsolved in rounds 2 through 4. The pipeline also produces verifier and meta-verifier traces following the proof-generation and proof-verification prompting style described in the [DeepSeekMath-V2 paper](https://arxiv.org/abs/2511.22570).
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+ Full details about dataset construction can be found in our technical report [An Open Recipe for IMO Gold: Training Nemotron for Olympiad Mathematics](https://arxiv.org/abs/2609.10712).
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  This dataset is ready for commercial or non-commercial uses.
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  Generated token counts are taken from the inference metadata. Input token counts are pipeline estimates based on prompt text length rather than a tokenizer re-count.
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  ## Reference(s):
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+ * [An Open Recipe for IMO Gold: Training Nemotron for Olympiad Mathematics](https://arxiv.org/abs/2609.10712) (technical report)
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  * [DeepSeek-V4-Pro](https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro)
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  * [nvidia/Nemotron-Math-Proofs-v1](https://huggingface.co/datasets/nvidia/Nemotron-Math-Proofs-v1)
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  * [nvidia/Nemotron-Math-Proofs-v2](https://huggingface.co/datasets/nvidia/Nemotron-Math-Proofs-v2)