Posts tagged Reinforcement Learning
Day-0 Support for the SGLang-Native RL Framework - slime on AMD Instinct™ GPUs
- 25 September 2025
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 efficient execution on AMD Instinct™ GPUs. Architecturally, slime supports two training modes: synchronous and asynchronous. Across these modes, we additionally present system-level optimizations with the corresponding use cases. Specifically, in the synchronous setting, our rollout optimizations deliver a 40% throughput improvement over the one without it on AMD Instinct™ GPUs. In the asynchronous setting, we develop a multi-turn RL agent framework to train the kernel generation model. You can also read more about this support in the MLsys – SGLang official blog.
Exploring Use Cases for Scalable AI: Implementing Ray with ROCm Support for Efficient ML Workflows
- 10 September 2025
In this blog, you will learn how to use Ray to easily scale your AI applications from your laptop to multiple AMD GPUs.
Aligning Mixtral 8x7B with TRL on AMD GPUs
- 12 June 2025
Building a ChatGPT-like assistant is a multi-step process that starts with pre-training a large language model (LLM) on internet-scale data across clusters of thousands of GPUs, resulting in what is known as a “base model”. This base model is then refined through an instruction based supervised fine-tuning (SFT) process, which trains it to function as a useful digital assistant capable of understanding and responding accurately to a wide range of queries. Finally, human preference alignment is applied to enhance the model’s friendliness, helpfulness, and safety, ensuring that interactions are not only informative but also pleasant for users. This combination of techniques creates a sophisticated assistant that is both powerful and user-centric—exemplified by AMD’s new Instella-Long assistant.
Reinforcement Learning from Human Feedback on AMD GPUs with verl and ROCm Integration
- 24 April 2025
In this blog post, we provide an overview of Volcano Engine Reinforcement Learning for LLMs (verl) and discuss its benefits in large-scale reinforcement learning from human feedback (RLHF). We also detail the modifications made to the codebase to optimize verl’s performance on AMD Instinct GPUs. Next, we walk through the process of building the Docker image using a Dockerfile on the user side, along with training scripts tailored for both single-node and multi-node setups. Lastly, we present verl’s performance results, focusing on throughput and convergence accuracy achieved on AMD Instinct™ MI300X GPUs. Follow this guide to get started with verl on AMD Instinct GPUs and accelerate your RLHF training with ROCm-optimized performance.
Panoptic segmentation and instance segmentation with Detectron2 on AMD GPUs
- 23 May 2024
23, May 2024 by .
GPU Unleashed: Training Reinforcement Learning Agents with Stable Baselines3 on an AMD GPU in Gymnasium Environment
- 11 April 2024
11 Apr, 2024 by .