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The post-training inference model SU-01 achieves gold medal performance on Olympiad-level exam questions
The approach includes three steps: first, supervised fine-tuning using a reverse perplexity curriculum to instill strict proof search and self-checking behaviors;
then, expanding these behaviors through two-stage reinforcement learning (transitioning from verifiable reward reinforcement learning to proof-level reinforcement learning);
finally, improving performance through scaling during testing.
The research team applied the method to a 30B-A3B backbone model, using approximately 340k sub-8K token trajectories for supervised fine-tuning, followed by 200 steps of reinforcement learning to obtain SU-01.
This model can perform stable reasoning on difficult problems, with trajectory lengths exceeding 100k tokens, achieving gold medal levels in competitions such as IMO 2025/USAMO 2026 and IPhO 2024/2025, and demonstrating generalization capabilities beyond mathematics and physics in scientific reasoning.
(Source: InFoQ)