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

The International Olympiad in Informatics (IOI) and the International Mathematical Olympiad (IMO) test different skills. IOI requires algorithms and code that pass hidden tests under strict time and submission limits. IMO demands rigorous natural-language proofs. Success at either competition is difficult. Success at both points to something broader.

Our recent results show that Nemotron is a strong, adaptable foundation for building world-class specialist models. Starting from Nemotron 3, our teams used supervised fine-tuning (SFT), reinforcement learning (RL), and feedback-driven inference to create systems that reached gold-medal level at both IMO 2026 and IOI 2026.

Competition	Nemotron specialization	Result
IOI 2026	Nemotron-3-Ultra-CC with SFT and GenCorrect	535.4/600, above the 361.12 gold threshold and the top human score of 498.27
IMO 2026	Nemotron 3 Ultra general, SFT, and RL checkpoints in a generate-verify-refine system	30/42, above the official gold threshold of 29

The IOI result came from a live, prospective run under the same time, internet-access, and submission constraints as human contestants. It was an unofficial, unsupervised benchmark and was not included in the official IOI ranking. The IMO system’s submitted proofs were graded by official IMO graders.

A reusable specialization recipe

"Easy to fine-tune" should mean more than making a checkpoint trainable. It should mean that a capable foundation model can be adapted to a demanding domain with a clear, reusable recipe.

Across the two projects, that recipe had four parts:

1. Start with a strong Nemotron base model.
2. Curate domain-specific problems and high-quality reasoning traces.
3. Apply standard post-training methods such as SFT and, where useful, RL.
4. Pair the specialist model with an inference loop that generates, evaluates, and improves candidate answers.