--- title: "ROLL vs OpenRLHF" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/alibaba-roll-vs-openrlhf-openrlhf" tools: ["alibaba-roll", "openrlhf-openrlhf"] --- # ROLL vs OpenRLHF *GraphCanon updated Aug 7, 2026* ## Verdict Pick ROLL if efficient library for scaling reinforcement learning tasks with large language models; user-friendly setup and debugging tools provided; pick OpenRLHF if openRLHF is a reinforcement learning framework designed for efficient distributed scheduling and large-scale model training up to 70B+ parameters, leveraging Ray for resource management and integrating vLLM, DeepSpeed, H. [ROLL](https://alibaba.github.io/ROLL/) reports 3.4k GitHub stars, 304 forks, and 120 open issues, last pushed Aug 7, 2026. [OpenRLHF](https://openrlhf.readthedocs.io/) has 9.9k stars, 996 forks, and 367 open issues, last pushed Jul 14, 2026. Figures are from public GitHub metadata via [ROLL's repository](https://github.com/alibaba/ROLL) and [OpenRLHF's repository](https://github.com/OpenRLHF/OpenRLHF). | | [ROLL](/tools/alibaba-roll.md) | [OpenRLHF](/tools/openrlhf-openrlhf.md) | | --- | --- | --- | | Tagline | Scaling Library for Reinforcement Learning with Large Language Models | Easy-to-use, Scalable and High-performance Agentic RL Framework based on Ray | | Stars | 3,354 | 9,891 | | Forks | 304 | 996 | | Open issues | 120 | 367 | | Language | Python | Python | | Adopt for | Efficient library for scaling reinforcement learning tasks with large language models; user-friendly setup and debugging tools provided. | OpenRLHF is a reinforcement learning framework designed for efficient distributed scheduling and large-scale model training up to 70B+ parameters, leveraging Ray for resource management and integrating vLLM, DeepSpeed, H | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | Evaluation & Observability, Model Training | Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ROLL](/tools/alibaba-roll.md) | [OpenRLHF](/tools/openrlhf-openrlhf.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 0d | 24d | | Open issues (now) | 120 | 367 | | Stars delta | Unknown | +132 (30d) | | Open issues delta | Unknown | +25 (30d) | | Full report | [trust report](/tools/alibaba-roll/trust.md) | [trust report](/tools/openrlhf-openrlhf/trust.md) | ## Decision facts: ROLL - **Adopt for:** Efficient library for scaling reinforcement learning tasks with large language models; user-friendly setup and debugging tools provided. ## Decision facts: OpenRLHF - **Pricing:** freemium - OpenRLHF is primarily available free of cost under the Apache-2.0 license, but users might incur costs based on their infrastructure usage for distributed training setups (e.g., cloud GPU instances). - **Adopt for:** OpenRLHF is a reinforcement learning framework designed for efficient distributed scheduling and large-scale model training up to 70B+ parameters, leveraging Ray for resource management and integrating vLLM, DeepSpeed, H ## Choose when ### Choose ROLL if… - Tags unique to ROLL: agentic, rlhf, rlvr. - Also covers Evaluation & Observability. - When developing reinforcement learning applications requiring integration of large language models, offering efficient scalability solutions. ### Choose OpenRLHF if… - Pricing: OpenRLHF is primarily available free of cost under the Apache-2.0 license, but users might incur costs based on their infrastructure usage for distributed training setups (e.g., cloud GPU instances).. - Tags unique to OpenRLHF: large language models, proximal-policy-optimization, raylib, reinforcement-learning. - Also covers Inference & Serving. - When you require high-throughput sample generation with minimal idle time on limited hardware due to its hybrid engine scheduling that enables sharing of GPU resources between models and vLLM engines. ## When NOT to use ROLL - Avoid for tasks that prioritize minimalist setups over advanced feature integrations like Alibaba Cloud Function Compute DevPods. - Not suitable if you prefer tools without built-in support for converting models between MCoreAdapter and Hugging Face formats. ## When NOT to use OpenRLHF - If your project does not require distributed training or large-scale model parameters (above 70B), as OpenRLHF is specifically optimized for scenarios where efficient distribution across multiple GPUs - When your environment cannot support Ray or vLLM, as these are crucial components of the framework for scheduling and high-performance sample generation, respectively. - If minimal Docker setup and hardware requirements with GPU constraints are not acceptable in your scenario. ## Common questions ### What is the difference between ROLL and OpenRLHF? ROLL: Scaling Library for Reinforcement Learning with Large Language Models. OpenRLHF: Easy-to-use, Scalable and High-performance Agentic RL Framework based on Ray. See the comparison table for live GitHub stats and shared categories. ### When should I choose ROLL over OpenRLHF? Choose ROLL over OpenRLHF when Tags unique to ROLL: agentic, rlhf, rlvr; Also covers Evaluation & Observability; When developing reinforcement learning applications requiring integration of large language models, offering efficient scalability solutions. ### When should I choose OpenRLHF over ROLL? Choose OpenRLHF over ROLL when Pricing: OpenRLHF is primarily available free of cost under the Apache-2.0 license, but users might incur costs based on their infrastructure usage for distributed training setups (e.g., cloud GPU instances).; Tags unique to OpenRLHF: large language models, proximal-policy-optimization, raylib, reinforcement-learning; Also covers Inference & Serving; When you require high-throughput sample generation with minimal idle time on limited hardware due to its hybrid engine scheduling that enables sharing of GPU resources between models and vLLM engines. ### When should I avoid ROLL? Avoid for tasks that prioritize minimalist setups over advanced feature integrations like Alibaba Cloud Function Compute DevPods. Not suitable if you prefer tools without built-in support for converting models between MCoreAdapter and Hugging Face formats. ### When should I avoid OpenRLHF? If your project does not require distributed training or large-scale model parameters (above 70B), as OpenRLHF is specifically optimized for scenarios where efficient distribution across multiple GPUs When your environment cannot support Ray or vLLM, as these are crucial components of the framework for scheduling and high-performance sample generation, respectively. If minimal Docker setup and hardware requirements with GPU constraints are not acceptable in your scenario. ### Is ROLL or OpenRLHF more popular on GitHub? OpenRLHF has more GitHub stars (9,891 vs 3,354). Stars measure visibility, not whether either tool fits your constraints. ### Are ROLL and OpenRLHF open source? Yes - both are open-source projects on GitHub (ROLL: Apache-2.0, OpenRLHF: Apache-2.0). ### Where can I find alternatives to ROLL or OpenRLHF? GraphCanon lists graph-backed alternatives at [ROLL alternatives](/tools/alibaba-roll/alternatives) and [OpenRLHF alternatives](/tools/openrlhf-openrlhf/alternatives) ([ROLL markdown twin](/tools/alibaba-roll/alternatives.md), [OpenRLHF markdown twin](/tools/openrlhf-openrlhf/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/alibaba-roll-vs-openrlhf-openrlhf.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ROLL or OpenRLHF? ROLL: Very active. OpenRLHF: 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 ROLL and OpenRLHF? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ROLL trust report](/tools/alibaba-roll/trust); [OpenRLHF trust report](/tools/openrlhf-openrlhf/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=alibaba-roll`](/api/graphcanon/graph?tool=alibaba-roll) - 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/_