AI RESEARCH
RefineRL: Advancing Competitive Programming with Self-Refinement Reinforcement Learning
arXiv CS.AI
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ArXi:2604.00790v1 Announce Type: new While large language models (LLMs) have nstrated strong performance on complex reasoning tasks such as competitive programming (CP), existing methods predominantly focus on single-attempt settings, overlooking their capacity for iterative refinement. In this paper, we present RefineRL, a novel approach designed to unleash the self-refinement capabilities of LLMs for CP problem solving. RefineRL