The latest release of Ray 2.6 introduces several key enhancements across the ecosystem, focusing on improving real-time capabilities, distributed training, and persistence. Streaming responses in Ray Serve enable applications to return results incrementally, enhancing user experience for computationally expensive workloads like large language models. Batch requests are also supported, allowing users to utilize hardware resources more efficiently. Additionally, Ray Data's streaming lazily execution is integrated with Ray Train, reducing memory usage during training. The release also introduces a new multi-GPU Learner API in the PPO algorithm, providing a simpler and more powerful alternative. Furthermore, improvements have been made to ensure reliability and persistence of training artifacts, including support for cloud storage and NFS paths. Overall, these changes aim to enhance performance, stability, and ease of use for users, while reducing network latency and memory usage.