✨DRIVE: Data Curation Best Practices for Reinforcement Learning with Verifiable Reward in Competitive Code Generation
📝 Summary:
This study develops a two-stage reinforcement learning method for competitive code generation. It uses tailored data curation and a hard-focus curriculum, achieving state-of-the-art performance on competitive programming benchmarks.
🔹 Publication Date: Published on Nov 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.06307
• PDF: https://arxiv.org/pdf/2511.06307
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For more data science resources:
✓ https://t.me/DataScienceT
#ReinforcementLearning #CodeGeneration #DataCuration #MachineLearning #AIResearch
📝 Summary:
This study develops a two-stage reinforcement learning method for competitive code generation. It uses tailored data curation and a hard-focus curriculum, achieving state-of-the-art performance on competitive programming benchmarks.
🔹 Publication Date: Published on Nov 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.06307
• PDF: https://arxiv.org/pdf/2511.06307
==================================
For more data science resources:
✓ https://t.me/DataScienceT
#ReinforcementLearning #CodeGeneration #DataCuration #MachineLearning #AIResearch
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