verl-rl-training

Files (3)

• SKILL.md
• api-reference.md · references/
• troubleshooting.md · references/

SKILL.md (first lines)

# 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 clone https://github.com/volcengine/verl.git
cd verl && pip install -e .[vllm,math]
```

## Quick Start: GRPO Training

```bash
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

```python
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

```python
# 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

```yaml
# config/grpo_math.yaml

From Orchestra-Research/AI-Research-SKILLs, MIT.

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