RL Is Everything, Everywhere, All at Once (skypilot.ai)

Reinforcement learning (RL) frameworks are evolving into a standardized, all-encompassing architecture for training advanced language models, moving beyond the initial Reinforcement Learning from Human Feedback (RLHF) methods. Notably, recent developments like DeepSeek-R1's introduction of Generalized Reward Prediction Optimization (GRPO) for verifiable rewards are shaping the design of leading models such as Kimi K3, Cursor's Composer 2, and Cognition's SWE-1.7. This new RL paradigm utilizes a comprehensive pipeline that integrates inference, training, and sandbox environments, all functioning within a single feedback loop—making it crucial for scaling complex agentic AI systems. This transition is pivotal for the AI/ML community, as it addresses significant infrastructure challenges inherent in the orchestration of multiple RL components. The architecture decisions—such as colocation versus disaggregation and synchronous versus asynchronous operations—impact scalability, efficiency, and computational resource management. Disaggregated systems allow for the separation of training and inference on specialized hardware, optimizing performance but complicating scheduling. Additionally, emerging strategies for asynchronous execution have shown to considerably increase training speed while maintaining model performance. As organizations continue to refine their RL infrastructures, platforms like SkyPilot aim to streamline the management process, offering solutions for gang scheduling, isolation of workloads, and efficient resource utilization, ultimately advancing the capabilities of AI systems in the industry.