A Deep Dive into Scaling RL for Code Generation with Synthetic Data and Curricula
Abstract: Reinforcement learning (RL) has emerged as a powerful paradigm for improving large language models beyond supervised fine-tuning, yet sustaining performance gains at scale remains an open challenge, as data diversity and structure, rather than volume alone, become the limiting factor. This article discusses a novel approach to address this challenge.
Introduction
As the demand for more sophisticated artificial intelligence continues to grow, researchers are increasingly turning to reinforcement learning (RL) to enhance the capabilities of large language models. Traditional methods, primarily focusing on supervised fine-tuning, often fall short when it comes to maintaining performance improvements at scale. This limitation arises from the necessity for diverse and well-structured data, which cannot simply be achieved by increasing volume. In light of this, a new scalable multi-turn synthetic data generation pipeline has been introduced, which aims to refine the RL training process.
The Multi-Turn Synthetic Data Generation Pipeline
The innovative approach involves a teacher model that iteratively refines problems based on performance summaries from a student model. This process results in the generation of structured difficulty progressions without requiring any fine-tuning of the teacher model itself. The multi-turn mechanism stands in contrast to single-turn generation, providing significant improvements in:
- Yield of Valid Synthetic Problems: The multi-turn approach creates a higher quantity of valid synthetic problems, enhancing the dataset used for training.
- Curriculum-Based Training: By producing easier and harder variants of the same core task, it supports the establishment of a curriculum that can be used to guide the training process.
Systematic Study of RL Training Dynamics
In order to understand the interaction between task difficulty, curriculum scheduling, and environment diversity during RL training, systematic studies were conducted across several model families, including Llama3.1-8B Instruct and Qwen3-8B Base models. Additional scaling experiments have also been performed on the Qwen2.5-32B model.
Key Findings
The study yielded several crucial insights regarding the effectiveness of synthetic data augmentation:
- Improvement in In-Domain Performance: Synthetic augmentation consistently enhanced code generation performance within the established domain.
- Out-of-Domain Performance: In many cases, it also positively impacted performance on out-of-domain tasks, such as mathematical problem-solving.
- Curriculum Design Impact: The design of the curriculum and the diversity of the data played significant roles in shaping the dynamics of the RL training process.
Conclusion
The introduction of a scalable multi-turn synthetic data generation pipeline represents a significant step forward in addressing the challenges of data diversity and structure in reinforcement learning for code generation. As demonstrated in the studies, this method not only enhances in-domain performance but also shows promise in out-of-domain scenarios. The empirical insights gained from this research may pave the way for more effective RL training strategies in the future, ultimately contributing to the continuous advancement of AI capabilities.