verl-vla is an open-source framework built on verl that unifies human-in-the-loop data collection, fine-tuning, evaluation, and reinforcement learning for vision-language-action policies across cloud and edge resources.
A complete GRPO workflow using verl, Qwen3-30B-A3B, SGLang, and PyTorch FSDP ran for 100 consecutive steps on four AMD Instinct MI455X GPUs, establishing an end-to-end RL baseline for CDNA 5.
A release focused on higher-throughput diffusion rollout, reusable omni adapters, and broader recipe coverage.
RL post-training on AMD Instinct GPUs is here: a turnkey ROCm container, AITER-accelerated vLLM and SGLang rollout, and accuracy validated on both MI300 and MI350 series.
verl-SpeCo 0.1.0 adds native DSpark support and standalone draft model training, with reusable feature storage, seven algorithm backends, vLLM/SGLang integration, and GPU/Ascend NPU support.
Keep the Tinker Cookbook loop you know, and run SFT, RL, and distillation on verl-managed GPU workers you control.
verl now has its own blog. It exists so that the people who build features can explain them properly.
