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Computer Science > Artificial Intelligence arXiv:2604.

02721 (cs) [Submitted on 3 Apr 2026 (v1), last revised 5 Aug 2026 (this version, v3)] Title:GrandCode: Achieving Grandmaster Level in Competitive Programming via Agentic Reinforcement Learning Authors:Ornith Team: Xiaoya Li, Guoyin Wang, Songqiao Su, Chris Shum, Jiwei Li View a PDF of the paper titled GrandCode: Achieving Grandmaster Level in Competitive Programming via Agentic Reinforcement Learning, by Ornith Team: Xiaoya Li and 4 other authors View PDF HTML (experimental) Abstract:Competitive programming remains one of the last few human strongholds in coding against AI.

The best AI system to date still underperforms the best humans competitive programming: the most recent best result, Google's Gemini~3 Deep Think, attained 8th place even not being evaluated under live competition conditions.

In this work, we introduce GrandCode, a multi-agent RL system designed for competitive programming.

The capability of GrandCode is attributed to two key factors: (1) It orchestrates a variety of agentic modules (hypothesis proposal, solver, test generator, summarization, etc) and jointly improves them through post-training and online test-time RL; (2) We introduce Agentic GRPO specifically designed for multi-stage agent rollouts with delayed rewards and the severe off-policy drift that is prevalent in agentic RL.

GrandCode is the first AI system that consistently beats all human participants in live contests of competitive programming: in the most recent three Codeforces live competitions, i.e.

, Round~1087 (Mar 21, 2026), Round~1088 (Mar 28, 2026), and Round~1089 (Mar 29, 2026), GrandCode placed first in all of them, beating all human participants, including legendary grandmasters.

GrandCode shows that AI systems have reached a point where they surpass the strongest human programmers on the most competitive coding tasks.Comments: Tech Report; Pre-print Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2604.02721 [cs.AI] (or arXiv:2604.02721v3 [cs.

AI] for this version) https://doi.org/10.48550/arXiv.2604.

02721 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Jiwei Li [view email] [v1] Fri, 3 Apr 2026 04:26:56 UTC (9,653 KB) [v2] Mon, 13 Jul 2026 00:56:35 UTC (9,653 KB) [v3] Wed, 5 Aug 2026 18:24:09 UTC (9,653 KB) Full-text links: Access Paper: View a PDF of the paper titled GrandCode: Achieving Grandmaster Level in Competitive Programming via Agentic Reinforcement Learning, by Ornith Team: Xiaoya Li and 4 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: cs.

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