AI Research Skills Library is a collection of reusable instructions that guide AI agents through research and machine-learning engineering tasks, from finding ideas and writing papers to training, evaluation, and deployment. It is for configuring agents such as Claude Code, Codex, and Gemini to perform research workflows.
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 Orchestra-Research/AI-Research-SKILLs --skill verlgit clone --depth 1 https://github.com/Orchestra-Research/AI-Research-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/orchestra-research/ai-research-skills/verl)<a href="https://agentmods.dev/skills/orchestra-research/ai-research-skills/verl"><img src="https://agentmods.dev/badge/skills/orchestra-research/ai-research-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/orchestra-research/ai-research-skills/verl"><img src="https://agentmods.dev/badge/skills/orchestra-research/ai-research-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 Orchestra-Research/AI-Research-SKILLs (12,567 stars, last pushed 2mo 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.
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.
openrlhf-training
High-performance RLHF framework with Ray+vLLM acceleration. Use for PPO, GRPO, RLOO, DPO training of large models (7B-70B+). Built on Ray, vLLM, ZeRO-3. 2× faster than DeepSpeedChat with distributed architecture and GPU resource sharing.