title: Competitive Programming with Large Reasoning Models
summary: 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.
paper_authors._id: 67ac1b080686a1e0690741ce
paper_authors.name: OpenAI
paper_authors._id: 67ac1b080686a1e0690741d0
paper_authors.name: Ahmed El-Kishky
paper_authors._id: 67ac1b080686a1e0690741d1
paper_authors.name: Alexander Wei
paper_authors._id: 67ac1b080686a1e0690741d2
paper_authors.name: Andre Saraiva
paper_authors._id: 67ac1b080686a1e0690741d3
paper_authors.name: Borys Minaev
paper_authors._id: 67ac1b080686a1e0690741d4
paper_authors.name: Daniel Selsam
paper_authors._id: 67ac1b080686a1e0690741d5
paper_authors.name: David Dohan
paper_authors._id: 67ac1b080686a1e0690741d6
paper_authors.name: Francis Song
paper_authors._id: 67ac1b080686a1e0690741d7
paper_authors.name: Hunter Lightman
paper_authors._id: 67ac1b080686a1e0690741d8
paper_authors.name: Ignasi Clavera
paper_authors._id: 67ac1b080686a1e0690741d9
paper_authors.name: Jakub Pachocki
paper_authors._id: 67ac1b080686a1e0690741da
paper_authors.name: Jerry Tworek
paper_authors._id: 67ac1b080686a1e0690741db
paper_authors.name: Lorenz Kuhn
paper_authors._id: 67ac1b080686a1e0690741dc
paper_authors.name: Lukasz Kaiser
paper_authors._id: 67ac1b080686a1e0690741dd
paper_authors.name: Mark Chen
paper_authors._id: 67ac1b080686a1e0690741de
paper_authors.name: Max Schwarzer
paper_authors._id: 67ac1b080686a1e0690741df
paper_authors.name: Mostafa Rohaninejad
paper_authors._id: 67ac1b080686a1e0690741e0
paper_authors.name: Nat McAleese
paper_authors._id: 67ac1b080686a1e0690741e1
paper_authors.name: o3 contributors
paper_authors._id: 67ac1b080686a1e0690741e2
paper_authors.name: Oleg Mürk
paper_authors._id: 67ac1b080686a1e0690741e3
paper_authors.name: Rhythm Garg
paper_authors._id: 67ac1b080686a1e0690741e4
paper_authors.name: Rui Shu
paper_authors._id: 67ac1b080686a1e0690741e5
paper_authors.name: Szymon Sidor
paper_authors._id: 67ac1b080686a1e0690741e6
paper_authors.name: Vineet Kosaraju
paper_authors._id: 67ac1b080686a1e0690741e7
paper_authors.name: Wenda Zhou
paper_title: Competitive Programming with Large Reasoning Models
paper_summary: 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.
paper_ai_summary: General-purpose reinforcement learning applied to large language models outperforms domain-specific systems in complex coding and reasoning tasks, achieving top results in competitions without hand-crafted strategies.
paper_ai_keywords: reinforcement learning large language models LLMs general-purpose reasoning models domain-specific system hand-engineered inference strategies International Olympiad in Informatics IOI gold medal Codeforces rating competitive programming