One Model Family, Two Gold-Level Results: Fine-Tuning Nemotron for IOI and IMO
Teams fine-tuned the Nemotron model family using supervised fine-tuning, reinforcement learning, and feedback-driven inference. These adaptations enabled world-class specialist systems to achieve gold-medal level results at both IMO 2026 and IOI 2026. The approach demonstrates that capable foundation models can be adapted to demanding domains with reproducible recipes.
The Core Development
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 .
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.
How It Works & Technical Architecture
For competitive programming, we curated 22,000 problems and generated synthetic reasoning traces to train two specialists. Nemotron-3-Nano-CC, with 30 billion total parameters and 3 billion active parameters, received both SFT and RL. Nemotron-3-Ultra-CC, with 550 billion total parameters and 55 billion active parameters, received SFT.
Deployment & Availability
Both post-trained checkpoints outperformed the general-availability model in the development experiments. The SFT checkpoint was strongest in the first search round, while the RL checkpoint achieved the best overall single-checkpoint result. Their strengths were complementary, so the final system used both specialists alongside the general model.
Strategic Perspective & Ecosystem Impact
"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.
The training and inference runs were substantial, but the underlying approach is familiar and reproducible. We did not need to build a new foundation model for every challenge. We specialized Nemotron for the task.
The progression on IOI 2025 makes the value of specialization easy to see. Nano improved from 130 points before post-training to 280 after SFT and 291 after RL. With GenCorrect, our iterative generate-evaluate-refine strategy, it reached 468 points and crossed the gold threshold of 438.3. Ultra-CC reached 502 points with the same test-time strategy.
Why it matters
It shows that capable foundation models can be effectively specialized for complex domains using reproducible training and inference strategies rather than requiring new foundation models for every challenge.