通过简单统一的扩展实现奥赛金牌级推理
Achieving Gold-Medal-Level Olympiad Reasoning via Simple and Unified Scaling
本文提出一种将预训练推理模型转化为严格奥赛求解器的统一方法。该方法首先采用反向困惑度课程进行监督微调，以灌输严谨的证明搜索与自我检查行为；随后通过两阶段强化学习流程扩展这些能力，最终结合测试时扩展提升性能。基于此方案训练的30B参数模型SU-01，在仅使用约34万条短轨迹微调和200步强化学习后，能稳定处理超过10万token的长轨迹难题，并在IMO、USAMO、IPhO等数学与物理奥赛中达到金牌级表现，同时展现出向数学物理之外科学领域的强推理泛化能力。
IMO 金牌级推理模型又多了一个，SU-01 的方法干净统一，特别在超长推理链上的稳定性是真正突破，做推理模型训练和竞赛级 AI 的可以认真读一下。
Recent progress in reasoning models has substantially advanced long-horizon mathematical and scientific problem solving, with several systems now reaching gold-medal-level performance on International Mathematical Olympiad (IMO) and International Physics Olympiad (IPhO) problems. In this paper, we introduce a simple and unified recipe for converting a post-trained reasoning backbone into a rigorous olympiad-level solver. The recipe first uses a reverse-perplexity curriculum for SFT to instill rigorous proof-search and self-checking behaviors, then scales these behaviors through a two-stage RL pipeline that progresses from RL with verifiable rewards to more delicate proof-level RL, and finally boosts solving performance with test-time scaling. Applying this recipe, we train a 30B-A3B backbone with SFT on around 340K sub-8K-token trajectories followed by 200 RL steps. The resulting model, SU-01, supports stable reasoning on difficult problems with trajectories exceeding 100K tokens, 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.
来源：HuggingFace Daily Papers（社区热门论文） · arxiv.org