✨From Code Foundation Models to Agents and Applications: A Practical Guide to Code Intelligence

📝 Summary:
This paper provides a practical guide to code LLMs, covering their lifecycle from data to deployment. It examines techniques, analyzes various models, and discusses real-world challenges like correctness and security. Experiments on pre-training and fine-tuning are included.

🔹 Publication Date: Published on Nov 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.18538
• PDF: https://arxiv.org/pdf/2511.18538

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✓ https://xn--r1a.website/DataScienceT

#CodeLLMs #AI #MachineLearning #SoftwareEngineering #FoundationModels

✨Self-Execution Simulation Improves Coding Models

📝 Summary:
This work trains code LLMs to simulate program execution step-by-step using fine-tuning and reinforcement learning. This enables self-verification and iterative self-fixing, significantly improving competitive programming performance and outperforming standard reasoning methods.

🔹 Publication Date: Published on Mar 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.03253
• PDF: https://arxiv.org/pdf/2604.03253

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For more data science resources:
✓ https://xn--r1a.website/DataScienceT

#CodeLLMs #AI #ReinforcementLearning #DeepLearning #CompetitiveProgramming