Configure Verl backends and launch GRPO-based RL fine-tuning jobs.
npx skills add https://github.com/OpenLAIR/dr-claw --skill verl-rl-training-openlair
Please help me install this Agent Skill. Skill: verl-rl-training Source: https://github.com/OpenLAIR/dr-claw/tree/main/skills/post-training/verl Command: npx skills add https://github.com/OpenLAIR/dr-claw --skill verl-rl-training-openlair
Provides structured guidance for performing reinforcement learning-based fine-tuning of large language models using Verl (Volcano Engine RL), enabling scalable, production-grade RL workflows.
Launch a basic GRPO-based RL training job with Verl using your dataset and a base model.
High-intent search queries and answers about installing and using this skill.
Production RL training for LLMs requires orchestrating distributed training backends with inference rollout engines. Using Verl, you can configure these components to execute scalable post-training campaigns with algorithms like PPO and GRPO.
Supported reinforcement learning algorithms for LLM post-training include PPO, GRPO, RLOO, REINFORCE++, DAPO, and SPIN. These cover common RL scenarios, allowing you to select the appropriate method for your specific reward optimization workflow.
Yes, Verl works with FSDP, FSDP2, and Megatron-LM to provide flexible backend support for large-scale training. This allows you to leverage existing distributed computing infrastructure for your RL fine-tuning campaigns.
Yes, you can use vLLM and SGLang as rollout engines for RL fine-tuning, alongside HuggingFace Transformers. This flexibility allows you to optimize inference workloads across diverse backends during the training process.
To launch a basic GRPO training job with Verl, you need to configure your dataset and a base model within the provided quick start workflow. This initiates the RL training process using the GRPO algorithm for post-training.
Scaling RLHF workflows across enterprise infrastructure is best achieved by utilizing flexible training backends like FSDP2 and Megatron-LM paired with efficient rollout engines. Verl structures this orchestration to handle large-scale post-training campaigns effectively.
EnkeZMigrate RL training recipes from verl to Relax frameworks.
RachasumanthDeploy trained machine learning models for inference using vLLM, TGI, Ollama, and llama.cpp.
ovachieverApply PyTorch FSDP for sharded, mixed-precision distributed training of large models.
devMoezAutomates LLM post-training alignment with TRL using SFT, DPO, and PPO/GRPO workflows.
