RefineRL: Advancing Competitive Programming with Self-Refinement Reinforcement Learning
📰 ArXiv cs.AI
RefineRL advances competitive programming with self-refinement reinforcement learning for large language models
Action Steps
- Introduce self-refinement capabilities to large language models
- Implement Skeptical-Agent and refinement mechanisms
- Train models using reinforcement learning to optimize iterative refinement
- Evaluate RefineRL on competitive programming benchmarks
Who Needs to Know This
AI researchers and engineers on a team can benefit from RefineRL as it enhances the performance of large language models in competitive programming, while software engineers and data scientists can apply the techniques to improve their own problem-solving capabilities
Key Insight
💡 RefineRL's self-refinement capabilities can significantly improve the performance of large language models in competitive programming
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💡 RefineRL boosts competitive programming with self-refinement RL for LLMs
Key Takeaways
RefineRL advances competitive programming with self-refinement reinforcement learning for large language models
Full Article
Title: RefineRL: Advancing Competitive Programming with Self-Refinement Reinforcement Learning
Abstract:
arXiv:2604.00790v1 Announce Type: new Abstract: While large language models (LLMs) have demonstrated 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 introduces two key innovations: (1) Skeptical-Agen
Abstract:
arXiv:2604.00790v1 Announce Type: new Abstract: While large language models (LLMs) have demonstrated 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 introduces two key innovations: (1) Skeptical-Agen
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