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

Researchers developed a scalable multi-turn synthetic data generation pipeline using reinforcement learning to improve large language models' code generation capabilities. The approach uses teacher models to create structured difficulty progressions and curriculum-based training, showing consistent improvements in code generation across Llama3.1-8B and Qwen models.

• →Multi-turn synthetic data generation significantly improves the yield of valid synthetic problems compared to single-turn approaches.
• →The pipeline creates natural stepping stones with easier and harder variants of tasks without requiring teacher model fine-tuning.
• →Systematic testing across Llama3.1-8B Instruct and Qwen3-8B Base models shows consistent in-domain code improvements.
• →Curriculum design and data diversity jointly influence RL training dynamics for better model performance.
• →The approach addresses the key challenge that data structure and diversity, not just volume, are limiting factors in RL scaling.