Abstract
The structural gap between probabilistic LLM generation and deterministic schema requirements hinders automated workflows. We propose RL-Struct, a lightweight framework using Gradient Regularized Policy Optimization (GRPO) with a hierarchical reward function to align LLMs with structural constraints. This approach eliminates the critic network, reducing peak VRAM by 38% compared to PPO.
On complex JSON tasks, RL-Struct achieves 89.7% structural accuracy and 92.1% validity, significantly outperforming SFT and zero-shot baselines. We also report an emergent curriculum—a self-organized learning process where the model prioritizes syntax before semantics. Our model is publicly available at Hugging Face.
Blogger's Review: RL-Struct significantly enhances the reliability and efficiency of structured output through its innovative hierarchical reward mechanism and the removal of the critic network, especially excelling in complex tasks. This framework may pave the way for more efficient automated generation systems in the future.