verl-rl-traininglisted

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.
r1z4x/tezgah · ★ 0 · AI & Automation · score 78

Install: claude install-skill r1z4x/tezgah

# 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 ```bash # 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 c