Guide · Agent skill
How to use the verl RL Training for LLMs agent skill
Updated · written with AI from the project's own documentation and checked against our catalogue data
The verl RL Training skill gives an AI agent specific guidance for training large language models with reinforcement learning using verl, the library from ByteDance's Seed team. It is aimed at ML engineers and researchers running RLHF or GRPO post-training on GPU clusters. After following this guide you will know how to install the skill, how to ask for help with it in plain language, and what hardware and safety checks to plan for first.
verl RL Training for LLMsOrchestra-Research/AI-Research-SKILLs · MIT Download the skill Quick start
- 1Download the ZIP. Download the verl-rl-training skill ZIP from this page.
- 2Unzip into a skills folder. Unzip into ~/.claude/skills/ for personal use, or into your project's .claude/skills/ folder.
- 3Or upload in the Claude apps. Open Settings, go to Capabilities, then Skills, and upload the package.
- 4Confirm your hardware. Check that you have a GPU cluster, ideally 8+ GPUs, and the Python packages listed in the requirements.
- 5Ask a specific training question. Describe the algorithm, model, dataset and GPU count in one request.
- 6Review and run the commands yourself. Read each command and config key the agent gives you, then run them on your own infrastructure.
What the verl RL Training skill makes the agent do
Once the skill is loaded, the agent can give step-by-step guidance for RL post-training with verl. The skill's source describes coverage of choosing an algorithm such as GRPO or PPO, preparing a parquet dataset with prompt and reward_model columns, writing a YAML training config, launching single-node or multi-node Ray jobs, and picking a backend such as FSDP, Megatron-LM, vLLM or SGLang.
The skill is a guide for a specialist workflow. It does not run training by itself, and the source states that it does not run scripts or use the network.
- Algorithm choice: GRPO, PPO (GAE), REINFORCE++, RLOO, ReMax, OPO and others listed in the skill
- Dataset and reward function structure for math-style tasks
- YAML config keys for rollout, actor, critic and KL control
- Single-node and multi-node Ray launch steps
- Backend selection between FSDP, Megatron-LM, vLLM and SGLang
Install the verl RL Training skill
Download the ZIP for this skill from our page. You can install it in one of two ways. For a personal setup, unzip it into ~/.claude/skills/. For a single project, unzip it into that project's .claude/skills/ folder. Alternatively, in the Claude apps, open Settings, go to Capabilities, and upload the skill under Skills.
The package contains SKILL.md plus two reference files: references/api-reference.md and references/troubleshooting.md. Keep the folder structure intact so the agent can find the references.
- Personal install: unzip into ~/.claude/skills/
- Project install: unzip into your project's .claude/skills/
- Claude apps: Settings → Capabilities → Skills, then upload
Check the requirements before you trigger it
The skill assumes a serious GPU setup. The source suggests 8 or more GPUs with an H100 recommended, and its workflows include multi-node Ray clusters. The software it references includes Python with pip, verl>=0.3.0, torch>=2.0.0, ray>=2.41.0, vllm>=0.8.2 and transformers>=4.40.0. Optional items are Docker, mbridge for Megatron, WandB or TensorBoard for monitoring, and HuggingFace Hub access to download base models.
The skill itself says that simple supervised fine-tuning or DPO is better served by TRL or Axolotl. If you do not have GPU access, the skill will still explain the concepts, but you cannot run its workflows there, and following them can incur large cloud costs.
- GPU cluster: 8+ GPUs suggested, H100 recommended
- Python with pip and the packages listed above
- Optional: Docker, mbridge, WandB or TensorBoard, HuggingFace Hub access
pip install verl[vllm]Trigger the skill with natural requests
You do not need a special command. Describe the training job you want in plain language and mention the algorithm or the task. The skill's description says it applies when you are implementing RLHF, GRPO, PPO or other RL algorithms for LLM post-training at scale.
Good requests name the algorithm, the model, the data and the hardware. Vague requests will get more general answers.
- "Help me set up a GRPO run with verl on Qwen2.5-7B for GSM8K math problems using 8 GPUs."
- "Write a verl YAML config for PPO with a critic model and GAE, and explain each key."
- "Compare FSDP and Megatron-LM backends in verl for a 70B model on a 4-node cluster."
- "My verl rollout is running out of CUDA memory. What should I reduce first?"
Example workflow: a GRPO math run
The skill's first workflow targets math reasoning with GRPO. It walks through a prerequisites checklist (8+ GPUs, parquet dataset with prompt and reward_model columns, base model from HuggingFace Hub), a dataset script, a rule-based reward function that extracts a boxed answer, a training config, a launch command, and monitoring in WandB or TensorBoard.
Ask the agent to adapt this workflow to your own dataset. It should point out which config keys you need to change, such as data.train_files and actor_rollout_ref.model.path.
python3 -m verl.trainer.main_ppo \
algorithm.adv_estimator=grpo \
data.train_files=~/data/gsm8k/train.parquet \
actor_rollout_ref.model.path=Qwen/Qwen2.5-7B \
actor_rollout_ref.rollout.n=8 \
actor_rollout_ref.actor.use_kl_loss=True \
trainer.n_gpus_per_node=8Safety, cost and troubleshooting notes
The skill declares that it runs no scripts and uses no network access. Commands it shows are for you to review and run yourself, on your own infrastructure. Check every command, path and cluster address before running it, especially multi-node launches that start Ray on several machines.
Out-of-memory errors are the most common problem the skill covers. Its troubleshooting reference suggests reducing log probability batch size, enabling gradient checkpointing, using FSDP2 with CPU offloading, or lowering vLLM gpu_memory_utilization from 0.9 to around 0.7.
- Review every command before running it; the skill does not execute anything
- Start with a small batch and a short run to estimate cost before scaling up
- For OOM during rollout, the troubleshooting reference suggests lowering log_prob_micro_batch_size, enabling gradient checkpointing, or lowering gpu_memory_utilization
What people use it for
- Setting up a GRPO math reasoning run on a single 8-GPU node
- Writing a PPO config with a critic model and GAE for a task with dense rewards
- Choosing between FSDP and Megatron-LM backends for a large model
- Planning a multi-node Ray launch on a GPU cluster
- Diagnosing CUDA out-of-memory errors during rollout or training
Frequently asked questions
Is the verl RL Training skill free?
The skill is published under the MIT licence, according to its source. The verl library itself is a separate install, and running training on GPUs has its own compute cost.
What do I need to use this skill?
You need Python with pip, the verl, torch, ray, vllm and transformers packages at the versions listed, and access to a GPU cluster, with 8+ GPUs suggested. Docker, mbridge, WandB or TensorBoard and HuggingFace Hub access are optional.
Can I use it without a GPU?
The skill's workflows assume 8+ GPUs and multi-node clusters, so it is not suited to beginners without GPU access. You can still ask the agent conceptual questions about GRPO and PPO.
How do I install verl itself?
The skill's source lists three options: pip install verl[vllm] (or verl[sglang] for the SGLang backend), a Docker image, or installing from the verl GitHub repository in editable mode. Pick the one that matches your environment.
Should I use this skill or TRL for fine-tuning?
Use TRL or Axolotl for simple supervised fine-tuning or DPO, as the skill itself recommends. Use verl when your goal is reinforcement learning such as GRPO or PPO at scale.
What are the alternatives to verl?
The skill names slime or miles for Megatron-native training, torchforge for PyTorch-native abstractions with Monarch, and TRL or Axolotl for simple SFT or DPO.
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Our verdict
This skill suits ML engineers and researchers who already run GPU clusters and want structured guidance for GRPO, PPO or similar RL post-training with verl. It is not a good fit for simple fine-tuning, DPO, or anyone without access to multiple GPUs, since the workflows can be costly to run.
Download the skill