A Deep Dive into Scaling RL for Code Generation with Synthetic Data and Curricula

📰 ArXiv cs.AI

Scaling RL for code generation with synthetic data and curricula improves large language models beyond supervised fine-tuning

advanced Published 26 Mar 2026

Action Steps

Introduce a scalable multi-turn synthetic data generation pipeline
Implement a teacher model to iteratively refine problems based on in-context learning
Use reinforcement learning to improve large language models beyond supervised fine-tuning
Evaluate the performance of the model on code generation tasks

Who Needs to Know This

AI engineers and ML researchers can benefit from this approach to improve the performance of large language models, and software engineers can apply the generated code in various applications

Key Insight

💡 Synthetic data and curricula can improve the performance of large language models beyond supervised fine-tuning