Verl Rl Training
Skill ActiveProvides 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.
To enable users to implement advanced LLM post-training techniques like RLHF, GRPO, and PPO at scale using the verl library.
Features
- Guidance for RLHF, GRPO, PPO, and other RL algorithms
- Support for large-scale LLM post-training
- Flexible infrastructure backend configurations
- Detailed installation and quick start examples
- Comprehensive configuration reference
Use Cases
- Implementing RLHF for LLM fine-tuning
- Training LLMs with GRPO for reasoning tasks
- Scaling PPO training for large language models
- Leveraging flexible backends like FSDP, Megatron-LM, and vLLM
Non-Goals
- Megatron-native training (recommends other tools)
- PyTorch-native abstractions with Monarch (recommends other tools)
- Simple SFT/DPO (recommends other tools)
Workflow
- Prepare Dataset
- Define Reward Function
- Create Training Config
- Launch Training
- Monitor and Validate
Practices
- Reinforcement Learning
- LLM Post-Training
- Distributed Systems
Prerequisites
- GPU cluster with 8+ GPUs (H100 recommended for math tasks)
- Dataset in parquet format with 'prompt' and 'reward_model' columns
- Base model from HuggingFace Hub
- Install Megatron-LM bridge (for Megatron workflow)
Trust
- warning:Issues AttentionIn the last 90 days, 17 issues were opened and 4 were closed, indicating a low closure rate and potentially slow maintainer response.
Installation
npx skills add davila7/claude-code-templatesRuns the Vercel skills CLI (skills.sh) via npx — needs Node.js locally and at least one installed skills-compatible agent (Claude Code, Cursor, Codex, …). Assumes the repo follows the agentskills.io format.
Quality Score
Trust Signals
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