AMD is excited to provide Day-0 support for the SGLang-native RL framework, slime. In this post, we will provide more details about our support and optimizations, as well as slime’s benefits for large-scale RL training. First, we describe the engineering efforts behind slime—including codebase modification, kernel-level memory management for ROCm™ software, and modifications to third-party dependencies (Megatron-LM, SGLang, and torch_memory_saver)—as well as Docker images that enable ...
