Achieving Gold-Medal-Level Olympiad Reasoning via Simple and Unified Scaling
Summary
A paper presenting SU-01, a 30B-A3B reasoning model that achieves gold-medal-level performance on IMO and IPhO problems via reverse-perplexity curriculum, two-stage reinforcement learning, and test-time scaling.
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Paper page - Achieving Gold-Medal-Level Olympiad Reasoning via Simple and Unified Scaling
Source: https://huggingface.co/papers/2605.13301 Published on May 13
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Abstract
A systematic approach transforms post-trained reasoning models into rigorous olympiad-level solvers through reverse-perplexity curriculum, two-stage reinforcement learning, and test-time scaling, achieving gold-medal performance on mathematical and physics competitions.
Recent progress inreasoning modelshas substantially advanced long-horizon mathematical andscientific problem solving, with several systems now reaching gold-medal-level performance onInternational Mathematical Olympiad(IMO) andInternational Physics Olympiad(IPhO) problems. In this paper, we introduce a simple and unified recipe for converting a post-trained reasoningbackboneinto a rigorous olympiad-level solver. The recipe first uses areverse-perplexity curriculumforSFTto instill rigorousproof-searchandself-checking behaviors, then scales these behaviors through a two-stageRLpipeline that progresses fromRLwithverifiable rewardsto more delicateproof-level RL, and finally boosts solving performance withtest-time scaling. Applying this recipe, we train a 30B-A3BbackbonewithSFTon around 340K sub-8K-tokentrajectories followed by 200RLsteps. The resulting model, SU-01, supports stable reasoning on difficult problems with trajectories exceeding 100Ktokens, while achieving gold-medal-level performance on mathematical and physical olympiad competitions, including IMO 2025/USAMO 2026 and IPhO 2024/2025. It also demonstrates strong generalization of scientific reasoning to domains beyond mathematics and physics.
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