Solvita: Enhancing Large Language Models for Competitive Programming via Agentic Evolution

Large language models (LLMs) still struggle with the rigorous reasoning demands of hard competitive programming. While recent multi-agent frameworks attempt to bridge this reliability gap, they remain fundamentally stateless: they rely on static retrieval and discard the valuable experience gained from solving previous problems and debugging their solutions. To address this, we present Solvita, an agentic evolution framework that enables continuous learning without requiring weight updates to the underlying LLM. Solvita coordinates the Planner, Solver, Oracle, and Hacker in a closed loop. Trainable knowledge networks use reinforcement learning from task feedback to improve the retrieval of prior experience. Solvita achieves the highest pass@3 in 14 of 15 combinations of models and benchmarks across CodeContests, APPS, and AetherCode. In paired comparisons, it performs comparably to commercial agents on their native backbones and achieves higher pass@3 in all 24 comparisons on other backbones. In controls using GPT-5.4 with matched budgets, learned Solver routing outperforms BM25 retrieval and LLM skill selection by 3.9 to 7.5 percentage points. On Codeforces, Solvita reaches a maximum rating estimate of 3394 using CodeElo, with gains of up to 526 points over matched bare backbones.