Topic

IMO

1 post about IMO. Every summary links to the original.

Paid picks we use ourselves. Links carry a referral code — a subscription through one earns us a commission at no extra cost to you.

Newest first

Posts

Hugging Face
One Model Family, Two Gold-Level Results: Fine-Tuning Nemotron for IOI and IMO
Starting from Nemotron 3, the team applied supervised fine-tuning (SFT) and reinforcement learning (RL) to create specialists for competitive programming and olympiad mathematics. At the IOI, the Nemotron-3-Ultra-CC model, trained with SFT on 22,000 curated problems and paired with GenCorrect iterative refinement, scored 535.4 of 600 points, exceeding both the gold threshold and the top human score. At the IMO, a combination of SFT and RL checkpoints scored 30 of 42 points by generating, critiquing, and refining natural-language proofs without external tools. Both projects followed a shared recipe: start with a strong base, curate domain-specific data, apply standard post-training, and tie the model to an inference loop that improves candidates. The results show that specialization and test-time compute together produce gold-level performance, and all models, data, and inference pipelines are available on Hugging Face.

The best engineering reads, once a week.

Topic

1 post about IMO. Every summary links to the original.

Paid picks we use ourselves. Links carry a referral code — a subscription through one earns us a commission at no extra cost to you.

Newest first

Hugging Face
One Model Family, Two Gold-Level Results: Fine-Tuning Nemotron for IOI and IMO
Starting from Nemotron 3, the team applied supervised fine-tuning (SFT) and reinforcement learning (RL) to create specialists for competitive programming and olympiad mathematics. At the IOI, the Nemotron-3-Ultra-CC model, trained with SFT on 22,000 curated problems and paired with GenCorrect iterative refinement, scored 535.4 of 600 points, exceeding both the gold threshold and the top human score. At the IMO, a combination of SFT and RL checkpoints scored 30 of 42 points by generating, critiquing, and refining natural-language proofs without external tools. Both projects followed a shared recipe: start with a strong base, curate domain-specific data, apply standard post-training, and tie the model to an inference loop that improves candidates. The results show that specialization and test-time compute together produce gold-level performance, and all models, data, and inference pipelines are available on Hugging Face.

The best engineering reads, once a week.