GAC: Stabilizing Asynchronous RL Training for LLMs via Gradient Alignment Control

Researchers propose GAC (Gradient Alignment Control), a new method to stabilize asynchronous reinforcement learning training for large language models. The technique addresses training instability issues that arise when scaling RL to modern AI workloads by regulating gradient alignment and preventing overshooting.