--- title: "awesome-RLHF vs OpenRLHF" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/opendilab-awesome-rlhf-vs-openrlhf-openrlhf" tools: ["opendilab-awesome-rlhf", "openrlhf-openrlhf"] --- # awesome-RLHF vs OpenRLHF *GraphCanon updated Aug 17, 2026* ## Verdict Pick awesome-RLHF if awesome-RLHF is a curated resource list focusing on reinforcement learning with human feedback (RLHF), which is crucial for refining large language models through interactive training methods; 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. [awesome-RLHF](https://github.com/opendilab/awesome-RLHF) reports 4.4k GitHub stars, 258 forks, and 6 open issues, last pushed May 20, 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 [awesome-RLHF's repository](https://github.com/opendilab/awesome-RLHF) and [OpenRLHF's repository](https://github.com/OpenRLHF/OpenRLHF). | | [awesome-RLHF](/tools/opendilab-awesome-rlhf.md) | [OpenRLHF](/tools/openrlhf-openrlhf.md) | | --- | --- | --- | | Tagline | A curated list of reinforcement learning with human feedback resources (continually updated) | Easy-to-use, Scalable and High-performance Agentic RL Framework based on Ray | | Stars | 4,422 | 9,891 | | Forks | 258 | 996 | | Open issues | 6 | 367 | | Language | - | Python | | Adopt for | awesome-RLHF is a curated resource list focusing on reinforcement learning with human feedback (RLHF), which is crucial for refining large language models through interactive training methods. | 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._ | | [awesome-RLHF](/tools/opendilab-awesome-rlhf.md) | [OpenRLHF](/tools/openrlhf-openrlhf.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Active (82%) | | Days since push | 89d | 24d | | Open issues (now) | 6 | 367 | | Stars delta | +9 (30d) | +132 (30d) | | Open issues delta | 0 (30d) | +25 (30d) | | Full report | [trust report](/tools/opendilab-awesome-rlhf/trust.md) | [trust report](/tools/openrlhf-openrlhf/trust.md) | ## Decision facts: awesome-RLHF - **Adopt for:** awesome-RLHF is a curated resource list focusing on reinforcement learning with human feedback (RLHF), which is crucial for refining large language models through interactive training methods. ## 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 awesome-RLHF if… - Tags unique to awesome-RLHF: deep-learning, depth-reinforcement-learning, human-feedback, rlhf. - Also covers Evaluation & Observability. - When you are specifically interested in the resources that pertain to enhancing reinforcement learning algorithms with human feedback for developing advanced AI systems. ### 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: proximal-policy-optimization, raylib, transformers, vllm. - 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 awesome-RLHF - If your focus is exclusively on generic deep-learning or reinforcement-learning resources without the aspect of integrating human feedback into the training process. ## 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 awesome-RLHF and OpenRLHF? awesome-RLHF: A curated list of reinforcement learning with human feedback resources (continually updated). 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 awesome-RLHF over OpenRLHF? Choose awesome-RLHF over OpenRLHF when Tags unique to awesome-RLHF: deep-learning, depth-reinforcement-learning, human-feedback, rlhf; Also covers Evaluation & Observability; When you are specifically interested in the resources that pertain to enhancing reinforcement learning algorithms with human feedback for developing advanced AI systems. ### When should I choose OpenRLHF over awesome-RLHF? Choose OpenRLHF over awesome-RLHF 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: proximal-policy-optimization, raylib, transformers, vllm; 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 awesome-RLHF? If your focus is exclusively on generic deep-learning or reinforcement-learning resources without the aspect of integrating human feedback into the training process. ### 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 awesome-RLHF or OpenRLHF more popular on GitHub? OpenRLHF has more GitHub stars (9,891 vs 4,422). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-RLHF and OpenRLHF open source? Yes - both are open-source projects on GitHub (awesome-RLHF: Apache-2.0, OpenRLHF: Apache-2.0). ### Where can I find alternatives to awesome-RLHF or OpenRLHF? GraphCanon lists graph-backed alternatives at [awesome-RLHF alternatives](/tools/opendilab-awesome-rlhf/alternatives) and [OpenRLHF alternatives](/tools/openrlhf-openrlhf/alternatives) ([awesome-RLHF markdown twin](/tools/opendilab-awesome-rlhf/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/opendilab-awesome-rlhf-vs-openrlhf-openrlhf.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, awesome-RLHF or OpenRLHF? awesome-RLHF: Steady. 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 awesome-RLHF and OpenRLHF? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-RLHF trust report](/tools/opendilab-awesome-rlhf/trust); [OpenRLHF trust report](/tools/openrlhf-openrlhf/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=opendilab-awesome-rlhf`](/api/graphcanon/graph?tool=opendilab-awesome-rlhf) - 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/_