/post-training-verl

name: post-training-verl
description: 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.
license: MIT
metadata:
  version: 1.0.0
  author: Orchestra Research
  tags: [Reinforcement Learning, RLHF, GRPO, PPO, Post-Training, Distributed Training]
  dependencies: [verl>=0.3.0, torch>=2.0.0, ray>=2.41.0, vllm>=0.8.2, transformers>=4.40.0]

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:

┌─────────────────────────────────────────────────────────┐
│ Single-Process Controller (Ray)                         │
│ - Orchestrates: rollout → reward → train → sync        │
└─────────────────────┬───────────────────────────────────┘
                      │
┌─────────────────────▼───────────────────────────────────┐
│ Multi-Process Workers                                   │
│ ├── ActorRolloutRefWorker (policy + generation)        │
│ ├── CriticWorker (value estimation, PPO only)          │
│ └── RewardManager (model-based or rule-based rewards)  │
└─────────────────────────────────────────────────────────┘

---

Workflow 1: Math Reasoning with GRPO

Use this workflow for training reasoning models on math tasks like GSM8K or MATH.

Prerequisites Checklist

• [ ] GPU cluster with 8+ GPUs (H100 recommended)
• [ ] Dataset in parquet format with `prompt` and `reward_model` columns
• [ ] Base model from HuggingFace Hub

Step 1: Prepare Dataset

import pandas as pd

data = [
    {
        "prompt": [{"role": "user", "content": "What is 15 + 27?"}],
        "reward_model": {"ground_truth": "42"}
    },
    # ... more examples
]
df = pd.DataFrame(data)
df.to_parquet("train.parquet")

Step 2: Define Reward Function

# reward_function.py
import re

def compute_reward(responses, ground_truths):
    rewards = []
    for response, gt in zip(responses, ground_truths):
        # Extract answer from response
        match = re.search(r'\\boxed{([^}]+)}', response)
        if match and match.group(1).strip() == gt.strip():
            rewards.append(1.0)
        else:
            rewards.append(0.0)
    return rewards

Step 3: Create Training Config

# config/grpo_math.yaml
algorithm:
  adv_estimator: grpo
  gamma: 1.0
  lam: 1.0

data:
  train_files: /path/to/train.parquet
  val_files: /path/to/val.parquet
  train_batch_size: 256
  max_prompt_length: 512
  max_response_length: 2048

actor_rollout_ref:
  model:
    path: Qwen/Qwen2.5-7B-Instruct
  actor:
    use_kl_loss: true
    kl_loss_coef: 0.001
    ppo_mini_batch_size: 64
  rollout:
    name: vllm
    n: 8  # samples per prompt
    temperature: 0.7
    top_p: 0.95

trainer:
  total_epochs: 3
  n_gpus_per_node: 8
  save_freq: 100

Step 4: Launch Training

python3 -m verl.trainer.main_ppo \
    --config-path config \
    --config-name grpo_math \
    trainer.experiment_name=grpo_math_qwen7b

Step 5: Monitor and Validate

• [ ] Check WandB/TensorBoard for loss curves
• [ ] Verify reward is increasing over steps
• [ ] Run evaluation on held-out test set

---

Workflow 2: PPO with Critic Model

Use this workflow when you need value-based advantage estimation (GAE).

Key Differences from GRPO

• Requires separate critic model
• Uses Generalized Advantage Estimation (GAE)
• Better for tasks with dense rewards

Configuration

algorithm:
  adv_estimator: gae  # Use GAE instead of GRPO
  gamma: 0.99
  lam: 0.95

critic:
  model:
    path: Qwen/Qwen2.5-7B-Instruct  # Can be same or different from actor
  ppo_mini_batch_size: 64

actor_rollout_ref:
  actor:
    use_kl_loss: true
    kl_loss_coef: 0.02
    clip_ratio: 0.2  # PPO clipping

Launch with Critic

python3 -m verl.trainer.main_ppo \
    algorithm.adv_estimator=gae \
    critic.model.path=Qwen/Qwen2.5-7B-Instruct \
    trainer.n_gpus_per_node=8

---

Workflow 3: Large-Scale T