Post-Training Language Models for Gold-Medal Performance in Coding Competitions

2026-09-02

AI Summary

本文提出一种结合大规模问题整理、合成推理轨迹、监督微调及强化学习的方法，用于提升语言模型在编程竞赛中的表现，并通过GenCorrect策略进一步优化解决方案。

Abstract

Competitive programming has become a key test of large language model reasoning, with international competitions such as IOI and ICPC representing its most challenging settings. We present an end-to-end specialization pipeline combining large-scale problem curation, synthetic reasoning traces, supervised fine-tuning (SFT), and reinforcement learning (RL). Using 22,000 curated problems, we train Nemotron-3-Nano-CC (30B-A3B) with SFT and RL and Nemotron-3-Ultra-CC (550B-A55B) with SFT alone. We further introduce GenCorrect, a feedback-driven test-time compute strategy that iteratively generates, evaluates, and refines diverse solutions. On IOI 2025, Nano-CC improves from 130 points to 291 after post-training and to 468 with GenCorrect, exceeding the gold threshold of 438.3 while Ultra-CC reaches 502. Guided by these results, we develop a competition-specific Ultra-CC system and evaluate it prospectively during IOI 2026. Under the same time, internet-access, and submission constraints as human contestants, it scores 535.4 out of 600, exceeding both the gold threshold of 361.12 and the top human score of 498.27. To our knowledge, this is the first AI system to outscore the highest-scoring human contestant on an IOI problem set.

Problem

• Competitive Programming
• Language Models
• Post-Training
• IOI
• Gold Medal Performance

Innovation

• end-to-end specialization pipeline
• synthetic reasoning traces
• supervised fine-tuning (SFT)
• reinforcement learning (RL)
• GenCorrect