Competitive Programming with Large Reasoning Models
Authors: name: OpenAI, name: Ahmed El-Kishky, name: Alexander Wei, name: Andre Saraiva, name: Borys Minaev, name: Daniel Selsam, name: David Dohan, name: Francis Song, name: Hunter Lightman, name: Ignasi Clavera, name: Jakub Pachocki, name: Jerry Tworek, name: Lorenz Kuhn, name: Lukasz Kaiser, name: Mark Chen, name: Max Schwarzer, name: Mostafa Rohaninejad, name: Nat McAleese, name: o3 contributors, name: Oleg Mürk, name: Rhythm Garg, name: Rui Shu, name: Szymon Sidor, name: Vineet Kosaraju, name: Wenda Zhou

We show that reinforcement learning applied to large language models (LLMs)
significantly boosts performance on complex coding and reasoning tasks.
Additionally, we compare two general-purpose reasoning models - OpenAI o1 and
an early checkpoint of o3 - with a domain-specific system, o1-ioi, which uses
hand-engineered inference strategies designed for competing in the 2024
International Olympiad in Informatics (IOI). We competed live at IOI 2024 with
o1-ioi and, using hand-crafted test-time strategies, placed in the 49th
percentile. Under relaxed competition constraints, o1-ioi achieved a gold
medal. However, when evaluating later models such as o3, we find that o3
achieves gold without hand-crafted domain-specific strategies or relaxed
constraints. Our findings show that although specialized pipelines such as
o1-ioi yield solid improvements, the scaled-up, general-purpose o3 model
surpasses those results without relying on hand-crafted inference heuristics.
Notably, o3 achieves a gold medal at the 2024 IOI and obtains a Codeforces
rating on par with elite human competitors. Overall, these results indicate
that scaling general-purpose reinforcement learning, rather than relying on
domain-specific techniques, offers a robust path toward state-of-the-art AI in
reasoning domains, such as competitive programming.