--- title: "ReinFlow vs verl" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/reinflow-reinflow-vs-verl-project-verl" tools: ["reinflow-reinflow", "verl-project-verl"] --- # ReinFlow vs verl *GraphCanon updated Aug 24, 2026* ## Verdict Pick ReinFlow if integrates flow-based models with reinforcement learning for robotics tasks; pick verl if verl/HybridFlow is a specialized Python framework for post-training reinforcement learning (RL) that provides detailed documentation and reproducible baselines. It supports PPO and GRPO algorithms and includes Ray Trains. [ReinFlow](https://reinflow.github.io/) reports 359 GitHub stars, 34 forks, and 9 open issues, last pushed Apr 24, 2026. [verl](https://verl-project.github.io) has 23k stars, 4.4k forks, and 1.1k open issues, last pushed Aug 7, 2026. Figures are from public GitHub metadata via [ReinFlow's repository](https://github.com/ReinFlow/ReinFlow) and [verl's repository](https://github.com/verl-project/verl). | | [ReinFlow](/tools/reinflow-reinflow.md) | [verl](/tools/verl-project-verl.md) | | --- | --- | --- | | Tagline | ReinFlow combines Flow Policy with online reinforcement learning for fine-tuning across various robotic tasks. | A Flexible and Efficient RL Post-Training Framework | | Stars | 359 | 22,854 | | Forks | 34 | 4,353 | | Open issues | 9 | 1,074 | | Language | Python | Python | | Adopt for | Integrates flow-based models with reinforcement learning for robotics tasks | verl/HybridFlow is a specialized Python framework for post-training reinforcement learning (RL) that provides detailed documentation and reproducible baselines. It supports PPO and GRPO algorithms and includes Ray Trains | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | Evaluation & Observability, Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ReinFlow](/tools/reinflow-reinflow.md) | [verl](/tools/verl-project-verl.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Very active (96%) | | Days since push | 121d | 0d | | Open issues (now) | 9 | 1.1k | | Stars delta | +10 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Owner type | User | Organization | | Full report | [trust report](/tools/reinflow-reinflow/trust.md) | [trust report](/tools/verl-project-verl/trust.md) | ## Decision facts: ReinFlow - **Adopt for:** Integrates flow-based models with reinforcement learning for robotics tasks ## Decision facts: verl - **Pricing:** freemium - verl operates under the Apache-2.0 license and is free and open-source. However, you might incur costs associated with cloud services like AWS SageMaker if you plan to deploy large-scale projects on a - **Requirements:** Min 8 GB RAM; Ensure your development environment supports Python and the backend systems you intend to use (FSDP or Megatron-LM). - **Adopt for:** verl/HybridFlow is a specialized Python framework for post-training reinforcement learning (RL) that provides detailed documentation and reproducible baselines. It supports PPO and GRPO algorithms and includes Ray Trains ## Choose when ### Choose ReinFlow if… - License: ReinFlow is MIT, verl is Apache-2.0. - Tags unique to ReinFlow: actorcritic, fine-tuning, finetuning-rl, flowmatching. - Also covers Evaluation & Observability. - Need to fine-tune policies in robotic manipulation or locomotion ### Choose verl if… - License: verl is Apache-2.0, ReinFlow is MIT. - Pricing: verl operates under the Apache-2.0 license and is free and open-source. However, you might incur costs associated with cloud services like AWS SageMaker if you plan to deploy large-scale projects on a. - Requirements: Min 8 GB RAM; Ensure your development environment supports Python and the backend systems you intend to use (FSDP or Megatron-LM).. - Tags unique to verl: grpo, post-training, ppo, python. - Opt for verl if your project requires flexibility in integrating advanced backend systems like FSDP or Megatron-LM to extend RL model capabilities. ## When NOT to use ReinFlow - Looking for solutions outside of robotic control tasks - Requiring offline reinforcement learning methods instead ## When NOT to use verl - Avoid verl if your project does not require advanced backend integration with systems like FSDP or Megatron-LM; it might be overkill and introduce unnecessary complexity. - Do not use if detailed documentation is less important to your workflow. While verl excels in this area, simpler frameworks may suffice for lighter requirements. ## Common questions ### What is the difference between ReinFlow and verl? ReinFlow: ReinFlow combines Flow Policy with online reinforcement learning for fine-tuning across various robotic tasks.. verl: A Flexible and Efficient RL Post-Training Framework. See the comparison table for live GitHub stats and shared categories. ### When should I choose ReinFlow over verl? Choose ReinFlow over verl when License: ReinFlow is MIT, verl is Apache-2.0; Tags unique to ReinFlow: actorcritic, fine-tuning, finetuning-rl, flowmatching; Also covers Evaluation & Observability; Need to fine-tune policies in robotic manipulation or locomotion. ### When should I choose verl over ReinFlow? Choose verl over ReinFlow when License: verl is Apache-2.0, ReinFlow is MIT; Pricing: verl operates under the Apache-2.0 license and is free and open-source. However, you might incur costs associated with cloud services like AWS SageMaker if you plan to deploy large-scale projects on a; Requirements: Min 8 GB RAM; Ensure your development environment supports Python and the backend systems you intend to use (FSDP or Megatron-LM).; Tags unique to verl: grpo, post-training, ppo, python; Opt for verl if your project requires flexibility in integrating advanced backend systems like FSDP or Megatron-LM to extend RL model capabilities. ### When should I avoid ReinFlow? Looking for solutions outside of robotic control tasks Requiring offline reinforcement learning methods instead ### When should I avoid verl? Avoid verl if your project does not require advanced backend integration with systems like FSDP or Megatron-LM; it might be overkill and introduce unnecessary complexity. Do not use if detailed documentation is less important to your workflow. While verl excels in this area, simpler frameworks may suffice for lighter requirements. ### Is ReinFlow or verl more popular on GitHub? verl has more GitHub stars (22,854 vs 359). Stars measure visibility, not whether either tool fits your constraints. ### Are ReinFlow and verl open source? Yes - both are open-source projects on GitHub (ReinFlow: MIT, verl: Apache-2.0). ### Where can I find alternatives to ReinFlow or verl? GraphCanon lists graph-backed alternatives at [ReinFlow alternatives](/tools/reinflow-reinflow/alternatives) and [verl alternatives](/tools/verl-project-verl/alternatives) ([ReinFlow markdown twin](/tools/reinflow-reinflow/alternatives.md), [verl markdown twin](/tools/verl-project-verl/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/reinflow-reinflow-vs-verl-project-verl.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ReinFlow or verl? ReinFlow: Slowing. verl: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for ReinFlow and verl? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ReinFlow trust report](/tools/reinflow-reinflow/trust); [verl trust report](/tools/verl-project-verl/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=reinflow-reinflow`](/api/graphcanon/graph?tool=reinflow-reinflow) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_