Provides a systematic blueprint for scaling Reinforcement Learning (RL) in LLMs using multi-turn synthetic data generation and difficulty-based curricula.
March 26, 2026
Original Paper
A Deep Dive into Scaling RL for Code Generation with Synthetic Data and Curricula
arXiv · 2603.24202
The Takeaway
As the field moves toward RL-based reasoning models (e.g., OpenAI's o1), this work provides critical insights into how to generate structured 'stepping stone' tasks. It reveals the necessary interplay between task difficulty and curriculum scheduling to sustain model improvement without manual labels.
From the 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. We address this by introducing a scalable multi-turn synthetic data generation pipeline in which a teacher model iteratively refines problems based on in-context student performance summaries, producing structured