One Model Family, Two Gold-Level Results: Fine-Tuning Nemotron for IOI and IMO

NVIDIA reports that fine-tuned Nemotron 3 models achieved gold-medal-level results in both the 2026 International Olympiad in Informatics and International Mathematical Olympiad, demonstrating how one foundation-model family can be adapted to distinct coding and proof-based reasoning tasks. For IOI, Nemotron-3-Ultra-CC, trained with supervised fine-tuning and paired with the GenCorrect generate-evaluate-refine process, scored 535.4 out of 600 in an unofficial live run conducted under contestant-like constraints, exceeding both the gold threshold and top human score. For IMO, a system combining general, supervised fine-tuned, and reinforcement-learning Nemotron 3 Ultra checkpoints generated, verified, criticized, and revised natural-language proofs to score 30 out of 42, above the official gold threshold. The projects used a common specialization approach involving strong base models, curated domain data and reasoning traces, post-training methods, and feedback-driven inference loops, with results suggesting that fine-tuning and test-time search are most effective when designed together. NVIDIA has released related models, datasets, benchmarks, papers, and inference pipelines through Hugging Face and the NeMo-Skills repository.

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