RL for massive LLMs faces update delays; optimizations like checkpoint engines reduce this to seconds.
Efficient RL updates enable scalable agent training, but 2025 research shows that reasoning optimization may be more critical than raw scaling. Integrate with frameworks like Ray RLlib while prioritizing chain-of-thought development over pure parameter updates.