•Flexible device mapping: Supports various placement of models onto different sets of GPUs for efficient resource utilization and scalability across different cluster sizes.
•Ready integration with popular HuggingFace models
•State-of-the-art throughput: SOTA LLM training and inference engine integrations and SOTA RL throughput.
•[2026/08] VeRL-Tinker is released: keep the Tinker Cookbook loop you know, and run SFT, RL, and distillation on verl-managed GPU workers you control; read the blog here.
•[2026/08] VeRL-Omni v0.2.0 is released: faster diffusion RL, rebuilt Qwen3-Omni multimodal training (DPO & GSPO), plus LTX-2.3, Qwen-Image-Edit support and more.
•[2026/05] uni-agent is released: a unified agent framework to build, run, and train LLM agents at scale, built on top of verl.
•[2026/05] VeRL-Omni is pre-released: a unified RL stack for diffusion and omni-modal model post-training built on top of verl. Read the blog post for details.
•[2026/04] verl's Megatron backend LoRA and router replay support is showcased at PyTorch Conference Europe 2026.
•[2026/03] verl is presented at NVIDIA GTC26: session#1, session#2
•[2026/01] verl has been migrated to the verl-project