Reinforcement Learning from Human Feedback on AMD GPUs with verl and ROCm 7.0.0

2026-02-12 · Source: AMD ROCm Blogs · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure · Depth: Intermediate, long

Summary

AMD has released an update on verl 0.6.0, an open-source Reinforcement Learning from Human Feedback (RLHF) framework, now optimized for AMD Instinct MI300X GPUs with ROCm 7.0.0 and vLLM 0.11.0.dev. This framework integrates high-throughput LLM training engines like FSDP and Megatron with inference engines such as vLLM and SGLang, utilizing Ray for hybrid orchestration. Benchmarks show that the AMD Instinct MI300X 8x GPU platform delivers higher PPO training throughput, up to 56% more for deepseek-llm-7b-chat and 36% more for Qwen2-7B-Instruct, compared to NVIDIA H100, while maintaining comparable convergence accuracy. For GRPO training, MI300X offers up to 12% higher throughput for deepseek-llm-7b-chat and 11% for Qwen2-7B-Instruct.

Key takeaway

For Machine Learning Engineers building or fine-tuning large language models with RLHF, consider adopting verl 0.6.0 on AMD Instinct MI300X GPUs. The demonstrated throughput advantages over NVIDIA H100, coupled with comparable convergence, suggest that AMD's platform offers a compelling alternative for scalable and efficient RLHF training. Explore the provided Docker images and training scripts to integrate verl into your workflow and potentially accelerate your LLM development.

Key insights

verl 0.6.0 with ROCm 7.0.0 enables efficient, scalable RLHF training on AMD Instinct MI300X GPUs.

Principles

• RLHF is critical for enhancing LLM reasoning.
• Hybrid orchestration optimizes resource utilization.
• AMD Instinct MI300X GPUs offer competitive RLHF throughput.

Method

The verl framework uses FSDP, Megatron, vLLM, SGLang, and Ray for parallel training and inference, with dynamic resource allocation to improve efficiency.

In practice

• Use provided Docker images for verl setup.
• Configure HIP_VISIBLE_DEVICES for AMD GPUs.
• Run PPO or GRPO algorithms with specified parameters.

Topics

• Reinforcement Learning from Human Feedback
• AMD Instinct MI300X GPUs
• ROCm Software
• verl Framework
• Large Language Models

Code references

• volcengine/verl
• ROCm/verl

Best for: Machine Learning Engineer, Deep Learning Engineer, AI Engineer

Related on AIssential

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• The Death of RLHF: A Practitioner’s Guide to the New Post-Training Stack — New post-training methods like GRPO, RLVR, and DAPO have replaced RLHF for reasoning tasks, improving scalability and efficiency.
• daVinci-kernel: Co-Evolving Skill Selection, Summarization, and Utilization via RL for GPU Kernel Optimization — A reinforcement learning framework co-evolves skill selection, summarization, and utilization for GPU kernel optimization.
• Efficient Exploration, Reasoning, and Training-Free MTP — Efficiency gains in LLMs are achievable through online RLHF, balanced reasoning control, and embedding-space multi-token prediction.

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Editorial summary, takeaway, and curation by AIssential. Original article published by AMD ROCm Blogs.