VeRL: ByteDance's Reinforcement Learning Framework for LLMs

The most exciting frontier in large language model research in 2025-2026 has not been about making models bigger. It has been about making them smarter through reinforcement learning. DeepSeek-R1 demonstrated that RL training – specifically GRPO (Group Relative Policy Optimization) – can dramatically improve a model’s reasoning capabilities, enabling chain-of-thought reasoning, self-correction, and structured problem solving that rivals much larger models. ByteDance, one of the world’s largest technology companies and the creator of TikTok and Douyin, has been applying these same techniques at scale to train its own models. VeRL is the framework behind that effort.

VeRL (Voltron Reinforcement Learning) is ByteDance’s open-source reinforcement learning framework designed specifically for LLM training. It implements state-of-the-art RL algorithms including PPO (Proximal Policy Optimization) and GRPO, integrates tightly with vLLM for efficient inference during training, and supports distributed training across hundreds of GPUs. VeRL is the production framework that powers ByteDance’s internal LLM development, including the Doubao (豆包) AI assistant.

What makes VeRL significant is its focus on the practical challenges of RL for LLMs. Training an LLM with RL is substantially more complex than supervised fine-tuning. It requires maintaining multiple model copies (actor, reference, reward, and optionally critic), generating rollouts (responses to evaluate), computing rewards, updating policy weights, and orchestrating all of this across distributed hardware. VeRL handles this complexity with an architecture that separates concerns cleanly while maximizing GPU utilization.

Core Architecture

VeRL’s architecture separates the three critical phases of RL training – rollout generation, reward computation, and policy update – into components that can be independently scaled:

Training Pipeline

The following diagram illustrates how VeRL orchestrates the RL training loop across distributed hardware:

flowchart TD
    subgraph Data[Data Pipeline]
        Dataset[Training Prompts]
        Buffer[Experience Buffer]
    end

    subgraph Inference[Rollout Generation]
        vLLM[vLLM Inference Engine]
        Actor[Actor Model<br>Policy to optimize]
    end

    subgraph Reward[Reward Computation]
        RM[Reward Model]
        PRM[Process Reward Model<br>Optional: Step-by-step]
    end

    subgraph Training[Training Engine]
        GRPO[GRPO<br>Group Relative Policy Optimization]
        PPO[PPO<br>Proximal Policy Optimization]
        Ref[Reference Model<br>KL divergence anchor]
    end

    subgraph Storage[Model Weights]
        NewWeights[Updated Policy]
        OldWeights[Current Policy]
    end

    Dataset --> vLLM
    vLLM --> Actor
    Actor -->|Generated responses| Buffer
    Buffer --> RM
    Buffer --> PRM
    RM -->|Reward scores| GRPO
    PRM -->|Step rewards| GRPO
    GRPO --> NewWeights
    NewWeights --> Actor
    Ref -->|KL penalty| GRPO

The three phases – rollout generation (left), reward computation (center), and policy update (right) – can be pipelined so that while one batch of prompts is being evaluated for rewards, the next batch is already generating rollouts. This overlapping execution maximizes GPU utilization and minimizes the wall-clock time per training iteration.

RL Algorithms Comparison

VeRL implements multiple RL algorithms, each suited for different training objectives:

GRPO has become the standout algorithm in 2025-2026, primarily because of its role in training DeepSeek-R1 and similar reasoning-focused models. By scoring groups of completions relative to each other rather than against an absolute scale, GRPO simplifies training and removes the need for a separate critic model.

Distributed Training Comparison

VeRL’s distributed training capabilities compared to other RL frameworks:

Getting Started

The VeRL GitHub repository provides installation instructions, configuration guides, and example training scripts. The project supports both single-node development (for testing with smaller models) and multi-node production deployment:

# Install VeRL
pip install verl

# Launch a training experiment
python examples/train_ppo.py --model Qwen2.5-7B --algorithm grpo

The vLLM inference engine is also a key dependency for VeRL’s rollout generation pipeline.

FAQ

What is VeRL?

VeRL (Voltron Reinforcement Learning) is ByteDance’s open-source framework for applying reinforcement learning to large language model training. It supports PPO, GRPO, and other RL algorithms with distributed training capabilities and native vLLM integration for efficient inference during training.

What is GRPO and why is it important?

GRPO (Group Relative Policy Optimization) is an RL algorithm that optimizes LLMs using grouped reward comparisons rather than a separate critic model. It simplifies the RL training pipeline, reduces memory requirements, and has been shown to improve reasoning capabilities – it was notably used in training DeepSeek-R1.

How does VeRL integrate with vLLM?

VeRL uses vLLM as its inference engine during RL training, enabling efficient token generation for the rollout phase. This tight integration means the actor model generates responses using vLLM’s optimized batching and KV-cache management, then VeRL computes rewards and updates the model weights.

What distributed training infrastructure does VeRL support?

VeRL supports multi-node training with tensor parallelism, pipeline parallelism, and data parallelism. It integrates with Ray for cluster orchestration and supports both FSDP (Fully Sharded Data Parallel) and ZeRO-3 for model sharding across GPUs.

Is VeRL used in production at ByteDance?

Yes. VeRL is the RL framework powering ByteDance’s internal LLM training pipelines, including the development of Doubao (豆包), ByteDance’s flagship AI assistant. The open-source release reflects the same code and architecture used in production at scale.

Can VeRL be used for RLHF?

Yes, VeRL supports RLHF (Reinforcement Learning from Human Feedback) through its PPO implementation, as well as RLAIF (RL from AI Feedback) through GRPO. The framework is designed to work with any reward model, whether trained from human preferences or LLM-generated feedback.

Further Reading

• VeRL GitHub Repository – Source code, documentation, and training examples
• DeepSeek-R1: Reinforcement Learning for Reasoning – The paper that popularized GRPO for LLM reasoning
• vLLM: High-Throughput LLM Serving – The inference engine integrated with VeRL
• Ray Distributed Computing – Cluster orchestration framework used by VeRL