Post-Training Language Models for Gold-Medal Performance in Coding Competitions
A coding-specialized model paired post-training with an iterative generate-evaluate-refine loop to exceed the top IOI 2026 human score. The reusable idea is feedback-driven test-time search.
The pipeline curated **22,000 problems** for SFT and RL, then used **GenCorrect** to generate, evaluate, and refine diverse solutions. Nano-CC rose from 130 to 468 points on IOI 2025; Ultra-CC reached 502.
For difficult coding tasks, allocate inference budget to multiple candidates, automated evaluation, and repair rather than relying on one completion. Specialized training data also mattered substantially before test-time search.
The pipeline curated **22,000 problems** for SFT and RL, then used **GenCorrect** to generate, evaluate, and refine diverse solutions. Nano-CC rose from 130 to 468 points on IOI 2025; Ultra-CC reached 502. For difficult coding tasks, allocate inference budget to multiple candidates, automated evaluation, and repair rather than relying on one completion. Specialized training data also mattered substantially before test-time search. The prospective IOI 2026 system scored **535.4/600**, above the top human’s 498.27 under the same stated constraints. Competition performance does not establish comparable reliability on repository-scale engineering.
This provides strong competition-specific evidence that specialized post-training and generate-evaluate-repair search can outperform a single completion. It shifts coding-model assessment toward the combined training-and-inference system, while explicitly limiting the result: exceptional algorithmic contest scores do not establish repository-scale engineering reliability.