Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add ihatesea69/HieuNghi-AI-Skills --skill verlgit clone --depth 1 https://github.com/ihatesea69/HieuNghi-AI-SkillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ihatesea69/hieunghi-ai-skills/verl)<a href="https://agentmods.dev/skills/ihatesea69/hieunghi-ai-skills/verl"><img src="https://agentmods.dev/badge/skills/ihatesea69/hieunghi-ai-skills/verl/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ihatesea69/hieunghi-ai-skills/verl"><img src="https://agentmods.dev/badge/skills/ihatesea69/hieunghi-ai-skills/verl.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00051 | $0.02768 |
| Opus 5 | $0.00026 | $0.01384 |
| Sonnet 5 | $0.00010 | $0.00554 |
| Haiku 4.5 | $0.00005 | $0.00277 |
Grade A, and why
verl-rl-training scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to verl-rl-training — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 392 lines — stays where its author put it; the contents beside it link to each section on GitHub.
verl: Volcano Engine Reinforcement Learning for LLMs
verl is a flexible, efficient, and production-ready RL training library for large language models from ByteDance's Seed team. It implements the HybridFlow framework (EuroSys 2025) and powers models like Doubao-1.5-pro achieving O1-level performance on math benchmarks.
When to Use verl
Choose verl when you need:
- Production-ready RL training at scale (tested up to 671B parameters)
- Flexibility to swap backends (FSDP ↔ Megatron-LM ↔ vLLM ↔ SGLang)
- Support for multiple RL algorithms (PPO, GRPO, RLOO, REINFORCE++, DAPO)
- Multi-turn rollout with tool calling for agentic workflows
- Vision-language model RL training
Consider alternatives when:
- You need Megatron-native training → use slime or miles
- You want PyTorch-native abstractions with Monarch → use torchforge
- You only need simple SFT/DPO → use TRL or Axolotl
Key Features
- Training backends: FSDP, FSDP2, Megatron-LM
- Rollout engines: vLLM, SGLang, HuggingFace Transformers
- Algorithms: PPO, GRPO, DAPO, RLOO, ReMax, REINFORCE++, SPIN, SPPO
- Models: Qwen-3, Llama-3.1, DeepSeek, Gemma-2 (0.5B to 671B)
- Advanced: LoRA RL, sequence parallelism, expert parallelism, multi-turn tools
Installation
# Option 1: pip install
pip install verl[vllm] # or verl[sglang] for SGLang backend
# Option 2: Docker (recommended for production)
docker pull verlai/verl:vllm011.latest
# Option 3: From source
git clone https://github.com/volcengine/verl.git
cd verl && pip install -e .[vllm,math]
Quick Start: GRPO Training
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=8
Core Architecture
verl uses a HybridFlow programming model separating control flow from computation:
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 392 lines · 51 tokens per session scan A 1167e2b2994c
verl-rl-training is a skill published in the GitHub repository ihatesea69/HieuNghi-AI-Skills (3 stars, last pushed 6mo ago), licensed MIT. It adds 51 tokens to every session and 2,768 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to verl-rl-training, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
verl-rl-training
Provides guidance for training LLMs with reinforcement learning using verl (Volcano Engine RL). Use when implementing RLHF, GRPO, PPO, or other RL algorithms for LLM post-training at scale with flexible infrastructure backends.
verl-rl-training
Provides guidance for training LLMs with reinforcement learning using verl (Volcano Engine RL). Use when implementing RLHF, GRPO, PPO, or other RL algorithms for LLM post-training at scale with flexible infrastructure backends.
verl-rl-training
Provides guidance for training LLMs with reinforcement learning using verl (Volcano Engine RL). Use when implementing RLHF, GRPO, PPO, or other RL algorithms for LLM post-training at scale with flexible infrastructure backends.
grpo-rl-training
Expert guidance for GRPO/RL fine-tuning with TRL for reasoning and task-specific model training.
verl-rl-training
Provides guidance for training LLMs with reinforcement learning using verl (Volcano Engine RL). Use when implementing RLHF, GRPO, PPO, or other RL algorithms for LLM post-training at scale with flexible infrastructure backends.
grpo-rl-training
Expert guidance for GRPO/RL fine-tuning with TRL for reasoning and task-specific model training.