veRL:why bother - chunhualiao/public-docs GitHub Wiki

Key Points

Research suggests verl is an open-source RL framework for LLMs, developed by ByteDance.
It seems likely that verl is optimized for LLM tasks, with integrations like PyTorch FSDP and vLLM.
The evidence leans toward using verl for LLM-specific RL, especially for reasoning and agent training.

What is verl: Volcano Engine Reinforcement Learning for LLMs?

verl, or Volcano Engine Reinforcement Learning for LLMs, is an open-source framework designed to make reinforcement learning (RL) training for large language models (LLMs) efficient and flexible. Developed by ByteDance's Seed team, it focuses on post-training tasks like fine-tuning LLMs with human feedback (RLHF), enhancing reasoning, and training agents. It integrates with popular LLM tools like PyTorch FSDP, Megatron-LM, and vLLM, making it suitable for researchers and practitioners working with LLMs.

When to Use verl Instead of Other RL Libraries?

You should consider verl when your project involves LLMs and requires specialized RL features. It's ideal for tasks like improving LLM reasoning, training agents, or handling large-scale models, thanks to its optimizations and integrations. For general RL tasks not involving LLMs, libraries like RLlib or Stable Baselines might be better, but for LLM-specific needs, verl offers unique advantages.

Survey Note: Detailed Analysis of verl and Its Use Cases

Introduction to verl

verl, standing for Volcano Engine Reinforcement Learning for LLMs, is an open-source framework initiated by ByteDance's Seed team and maintained by the verl community. Launched in 2023, it is designed specifically for reinforcement learning (RL) training of large language models (LLMs), focusing on post-training tasks such as reinforcement learning from human feedback (RLHF), reasoning enhancement, and agent-based interactions. It is the open-source implementation of the HybridFlow paper (HybridFlow: A Flexible and Efficient RLHF Framework), aiming to provide a flexible, efficient, and production-ready solution for LLM practitioners.

The framework is particularly noted for its ease of use, with the ability to extend diverse RL algorithms like PPO and GRPO in just a few lines of code, and its seamless integration with existing LLM infrastructures. Given the current date, May 26, 2025, verl has seen recent updates, including performance improvements in releases like v0.3.0.post1, and is actively discussed at events like PyTorch Day China in June 2025 and ICLR 2025 Expo.

Key Features and Technical Details

verl's design is tailored for the scale and complexity of LLMs, offering several unique features that distinguish it from general-purpose RL libraries. Below is a detailed breakdown:

Ease of Extension for RL Algorithms: verl employs a hybrid programming model that combines single-controller and multi-controller paradigms. This allows for flexible representation and efficient execution of complex post-training dataflows, enabling users to build RL workflows with minimal code. For instance, it supports algorithms like PPO, GRPO, and ReMax, making it versatile for various RL tasks.
Integration with LLM Infrastructures: It integrates seamlessly with popular LLM frameworks, including PyTorch Fully Sharded Data Parallel (FSDP), Megatron-LM, vLLM (version >=0.8.2), and SGLang. It also supports HuggingFace models, ensuring compatibility with widely used NLP ecosystems. This modularity decouples computation and data dependencies, making it easy to extend to other training and inference frameworks.
Scalability and Device Mapping: verl supports flexible device mapping, allowing various placements of models onto different sets of GPUs. This is crucial for scalability, supporting training up to 70B parameter models across hundreds of GPUs. It includes features like efficient actor model resharding with 3D-HybridEngine, which eliminates memory redundancy and reduces communication overhead during transitions between training and generation phases.
Performance and Throughput: The framework achieves state-of-the-art (SOTA) throughput for both LLM training and inference, integrating existing SOTA frameworks. Recent releases, such as v0.3.0.post1, boast a 1.4x speedup, and it supports optimizations like flash-attention, sequence packing, and long context support via DeepSpeed Ulysses. A performance tuning guide is available at verl Performance Tuning.
Additional Support: verl supports AMD (ROCm Kernel), experiment tracking with tools like wandb and mlflow, and upcoming features like DeepSeek 671b optimizations with Megatron v0.11. It also includes upgrade guides for vLLM, SGLang, and FSDP2, ensuring users can keep up with the latest developments.

The following table summarizes the key technical features:

Feature	Description
RL Algorithm Extension	Hybrid model for easy implementation of PPO, GRPO, etc.
LLM Integration	Supports PyTorch FSDP, Megatron-LM, vLLM, HuggingFace models
Scalability	Flexible GPU mapping, scales to 70B models, hundreds of GPUs
Performance	SOTA throughput, 1.4x speedup in recent releases, flash-attention support
Additional Features	AMD support, experiment tracking, upcoming DeepSeek optimizations

Use Cases and Community Engagement

verl has been adopted in a wide range of projects, demonstrating its versatility for LLM-related RL tasks. The "Awesome work using verl" section highlights several applications, as shown in the table below:

Project Name	Description	GitHub Link
TinyZero	Reproduces DeepSeek R1 Zero recipe for reasoning tasks	TinyZero GitHub
SkyThought	RL training for Sky-T1-7B by NovaSky AI team	SkyThought GitHub
simpleRL-reason	Investigates Zero RL for open base models	simpleRL-reason GitHub
Easy-R1	Multi-modal RL training framework	Easy-R1 GitHub
OpenManus-RL	LLM Agents RL tuning for multiple agent environments	OpenManus-RL GitHub
rllm	Async RL training with verl-pipeline	rllm GitHub
PRIME	Process reinforcement through implicit rewards	PRIME GitHub
RAGEN	General-purpose reasoning agent training framework	RAGEN GitHub
Logic-RL	Reproduces DeepSeek R1 Zero on 2K Tiny Logic Puzzle Dataset	Logic-RL GitHub
Search-R1	RL with reasoning and searching (tool-call) interleaved LLMs	Search-R1 GitHub
DeepRetrieval	RL Training of Search Agent with Search/Retrieval Outcome	DeepRetrieval GitHub
ReSearch	Learning to Reason with Search for LLMs via RL	ReSearch GitHub
Code-R1	Reproducing R1 for Code with Reliable Rewards	Code-R1 GitHub
Skywork-OR1	Skywork open reasoner series	Skywork-OR1 GitHub
ToRL	Scaling tool-integrated RL	ToRL GitHub
verl-agent	Scalable training for long-horizon LLM/VLM agents, new GiGPO algorithm	verl-agent GitHub
GUI-R1	Generalist R1-style Vision-Language Action Model For GUI Agents	GUI-R1 GitHub

These projects cover areas like reasoning, search, agent training, and multi-modal tasks, showcasing verl's applicability in cutting-edge LLM research. Recent developments, such as Seed-Thinking-v1.5 achieving 86.7 on AIME 2024 and VAPO scoring 60.4, highlight its performance in competitive benchmarks.

The community aspect is strong, with contributions welcome via GitHub, Slack, and WeChat channels. The project roadmap (verl Roadmap) and good first issues (Good First Issues) encourage participation, and it is licensed under Apache License 2.0, ensuring open collaboration.

When to Use verl Instead of Other RL Libraries

The choice of verl over other RL libraries, such as RLlib, Stable Baselines, or OpenRLHF, depends on the specific needs of the project. Research suggests that verl is particularly advantageous in the following scenarios:

LLM-Specific Tasks: If the project involves LLMs, verl's tailored features, such as integration with PyTorch FSDP, Megatron-LM, and vLLM, provide significant benefits. Other libraries may lack these specific optimizations for LLM scale and complexity.
Complex RL Dataflows: For tasks requiring complex RL workflows, such as RLHF, reasoning enhancement, or agent training, verl's hybrid programming model offers flexibility and efficiency. This is evident from its use in projects like ReSearch and DeepRetrieval, which focus on reasoning and search tasks.
High-Performance Requirements: When high throughput and efficient resource utilization are critical, especially in large-scale distributed training, verl's SOTA performance and features like 3D-HybridEngine resharding make it a strong choice. Recent releases, like v0.3.0.post1 with a 1.4x speedup, underscore this advantage.
Integration with HuggingFace Models: For users working within the HuggingFace ecosystem, verl's ready integration ensures a seamless workflow, which may not be as pronounced in general-purpose RL libraries.

In contrast, for general RL tasks not involving LLMs, libraries like RLlib or Stable Baselines might be more appropriate due to their broader applicability. However, for projects at the intersection of RL and LLMs, verl's specialized design and community support make it a compelling option. The evidence leans toward verl being particularly suitable for researchers and practitioners in the LLM domain, as seen in its adoption for tasks like TinyZero and SkyThought.

Recent Developments and Community Engagement

As of May 26, 2025, verl is actively evolving, with recent news including presentations at NeurIPS 2024, Ray Summit 2024, and acceptance to EuroSys 2025. Upcoming events like PyTorch Day China (June 7, 2025, Beijing) and ICLR 2025 Expo further highlight its relevance. The community is encouraged to contribute, with hiring opportunities for internships and full-time roles in RL for agents, contactable via [email protected]. Blogs, performance tuning guides, and reproducible algorithm baselines are available at verl Documentation, ensuring users have resources to get started.

Conclusion

verl stands out as a specialized framework for RL training of LLMs, offering unique features like easy algorithm extension, LLM infrastructure integration, and high-performance scalability. Its use cases span reasoning, agent training, and multi-modal tasks, making it ideal for LLM practitioners. When compared to other RL libraries, it is best suited for LLM-specific projects requiring advanced optimizations and community support, positioning it as a valuable tool in the evolving landscape of AI research.