[Submitted on 9 Sep 2026]
Title:An Open Recipe for IMO Gold: Training Nemotron for Olympiad Mathematics
View a PDF of the paper titled An Open Recipe for IMO Gold: Training Nemotron for Olympiad Mathematics, by Ivan Moshkov and 5 other authors
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Abstract:We study how model post-training and test-time inference design affect natural-language proof generation for hard olympiad mathematics. Starting from Nemotron 3 Ultra, we train two specialist checkpoints using supervised fine-tuning and reinforcement learning, and evaluate checkpoint choice, verification, and refinement. Based on these findings, we present an open-model test-time-compute pipeline. The system operates entirely in natural language, with no formal prover, external tools, or internet access. Three Nemotron 3 Ultra checkpoints - the general-availability model and two post-trained specialists - power an iterative search that generates, verifies, and refines candidate proofs; a separate high-compute stage then selects each final submission. The system scored 30 out of 42 points at IMO 2026, reaching the gold-medal threshold. We release the two post-trained checkpoints as well as the training data, the training and inference code, the submitted solutions, and Nemotron-IMO-Bench, a new benchmark of 200 novel olympiad-level problems.
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2609.10712 [cs.AI]
(or arXiv:2609.10712v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.10712
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Ivan Moshkov [view email] [v1] Wed, 9 Sep 2026 18:08:59 UTC (123 KB)
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