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

arXiv: https://arxiv.org/abs/2605.13301 · HTML: https://mlsanity.com/paper/2605.13301/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Placed in Mathematical reasoning, one of 17 directions in LLM reasoning.
- Builds on 2 of its 44 references that are in the map.
- First posted on arXiv 13 May 2026.

## Placed in

- LLM reasoning / [Mathematical reasoning](https://mlsanity.com/lr/mathematical-reasoning/)

## Builds on (most cited first, 2 of 2)

- [gpt-oss-120b & gpt-oss-20b Model Card](https://mlsanity.com/paper/2508.10925/) (2025)
- [Process Reinforcement through Implicit Rewards](https://mlsanity.com/paper/2502.01456/) (2025)

## Built on by (most cited first, 8 of 8)

- [SimpleOPD: Simple Tokenizer-Agnostic On-Policy Distillation for Long-Context Reasoning](https://mlsanity.com/paper/2608.14277/) (2026)
- [MaxProof: Scaling Mathematical Proof with Generative-Verifier RL and Population-Level Test-Time Scaling](https://mlsanity.com/paper/2606.13473/) (2026)
- [Parametric Skills](https://mlsanity.com/paper/2606.30015/) (2026)
- [Rethinking Critic Learning in PPO: Understanding and Mitigating Value Flattening](https://mlsanity.com/paper/2609.18708/) (2026)
- [Artificial Intelligence for Mathematical Reasoning: An Integrated Survey of Language Models, Neuro-symbolic Systems, and Verified Discovery](https://mlsanity.com/paper/2606.08728/) (2026)
- [ComBench: A Benchmark for Rigorous Proof Reasoning and Constructive Realization in Olympiad-Level Combinatorics](https://mlsanity.com/paper/2606.10479/) (2026)
- [UMoE:Unlocking Every Expert in Domain-Specific Training](https://mlsanity.com/paper/2607.11444/) (2026)
- [Priced Guidance: Can Language Models Generate Future Research Ideas?](https://mlsanity.com/paper/2610.04976/) (2026)

## Abstract (arXiv)

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.