🔥 Achieving Gold-Medal-Level Olympiad Reasoning via Simple and Unified Scaling

💡 The paper presents a systematic approach to transform post-trained reasoning models into rigorous olympiad-level solvers. The problem addressed is achieving gold-medal-level performance on mathematical and physics competitions. The method involves a simple and unified recipe that includes three main components: a reverse-perplexity curriculum, a two-stage reinforcement learning pipeline, and test-time scaling. The reverse-perplexity curriculum is used to instill rigorous proof-search and self-checking behaviors in the model. The two-stage reinforcement learning pipeline progresses from reinforcement learning with verifiable rewards to more delicate proof-level reinforcement learning, allowing the model to scale its behaviors. Finally, test-time scaling is used to boost the solving performance of the model.

The authors applied this recipe to a 30B-A3B backbone with sequence-to-function transformer training on around 340K sub-8K-token trajectories, followed by 200 reinforcement learning steps. The resulting model, SU-01, demonstrates stable reasoning on difficult problems with trajectories exceeding 100K tokens. The results show that the model achieves gold-medal-level performance on mathematical and physical olympiad competitions, including the International Mathematical Olympiad and the International Physics Olympiad. Additionally, the model demonstrates strong generalization of scientific reasoning to domains beyond mathematics and physics. Overall, the paper contributes a simple and unified approach to achieving gold-medal-level olympiad reasoning, with significant implications for advancing long-horizon mathematical and scientific problem solving.

📅 Published on May 13

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2605.13301
• PDF: https://arxiv.org/pdf/2605.13301
• Project Page: https://simplified-reasoning.github.io/SU-01

🤖 Models citing this paper:
• https://huggingface.co/Simplified-Reasoning/SU-01

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📢 By: https://xn--r1a.website/PaperNexus

#OlympiadReasoning #MathematicalCompetitions #PhysicsCompetitions #ReinforcementLearning #ArtificialIntelligence