Comparison
open-r1 vs OpenRLHF
Verdict
Pick open-r1 if open-R1 is an open-source effort to replicate DeepSeek-R1's models and training pipelines involving model distillation, RL pipeline replication, and multi-stage training; 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.
Markdown twin · open-r1 alternatives · OpenRLHF alternatives
GraphCanon updated 1w
vs
Trust & integrity
| Signal | open-r1 | OpenRLHF |
|---|---|---|
| Maintenance | Slowing (125d since push) As of 2w · github_public_v1 | Active (24d since push) As of 1w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Organization account As of 1w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | Published findings As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- open-r1
- Fully open reproduction of DeepSeek-R1
- OpenRLHF
- Easy-to-use, Scalable and High-performance Agentic RL Framework based on Ray
Stars
- open-r1
- 26k
- OpenRLHF
- 9.9k
Forks
- open-r1
- 2.4k
- OpenRLHF
- 996
Open issues
- open-r1
- 340
- OpenRLHF
- 367
Language
- open-r1
- Python
- OpenRLHF
- Python
Adopt for
- open-r1
- Open-R1 is an open-source effort to replicate DeepSeek-R1's models and training pipelines involving model distillation, RL pipeline replication, and multi-stage training.
- OpenRLHF
- 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
- open-r1
- -
- OpenRLHF
- -
Runtime
- open-r1
- -
- OpenRLHF
- -
License
- open-r1
- The project is licensed under Apache-2.0, providing a permissive license that allows for free use, modification, and distribution.
- OpenRLHF
- Apache-2.0
Last pushed
- open-r1
- Apr 2, 2026
- OpenRLHF
- Jul 14, 2026
Categories
- open-r1
- Inference & Serving, Model Training
- OpenRLHF
- Inference & Serving, Model Training
Trust and health
Maintenance
- open-r1
- Slowing (36%)
- OpenRLHF
- Active (82%)
Days since push
- open-r1
- 125d
- OpenRLHF
- 24d
Open issues (now)
- open-r1
- 340
- OpenRLHF
- 367
Stars delta
- open-r1
- Unknown
- OpenRLHF
- +132 (30d)
Open issues delta
- open-r1
- Unknown
- OpenRLHF
- +25 (30d)
OSV dependency advisories
- open-r1
- No lockfile (source not queried)
- OpenRLHF
- Published findings
Full report
- open-r1
- Trust report
- OpenRLHF
- Trust report
Shared compatibility
- Python · open-r1: Python runtime · OpenRLHF: Python runtime
Choose open-r1 if…
- Requirements: Min 8 GB RAM; Installation requires CUDA version 12.4 and PyTorch v2.6.0, with specific dependencies like vLLM and FlashAttention that are critical..
- Tags unique to open-r1: cuda, deepseek-r1, flashattention, model distillation.
- Use Open-R1 when you need a detailed understanding of how DeepSeek-R1 operates, considering the project closely mirrors its architecture and processes.
When NOT to use open-r1
- Avoid Open-R1 if your hardware does not support CUDA 12.4 or cannot run PyTorch `v2.6.0`, as this may lead to errors.
- Do not use it if the need for rapid experimentation outweighs the value of detailed replication, since the multi-stage training and datasets curation process can be time-consuming.
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.
- 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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (huggingface/open-r1) · observed Aug 6, 2026
- GitHub forks (huggingface/open-r1) · observed Aug 6, 2026
- Last push (huggingface/open-r1) · observed Apr 2, 2026
- License file (Apache-2.0) · observed Aug 6, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (OpenRLHF/OpenRLHF) · observed Aug 7, 2026
- GitHub forks (OpenRLHF/OpenRLHF) · observed Aug 7, 2026
- Last push (OpenRLHF/OpenRLHF) · observed Jul 14, 2026
- License file (Apache-2.0) · observed Aug 7, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: open-r1 26k · OpenRLHF 9.9k (synced Aug 6, 2026).
Common questions
- What is the difference between open-r1 and OpenRLHF?
- open-r1: Fully open reproduction of DeepSeek-R1. 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 open-r1 over OpenRLHF?
- Choose open-r1 over OpenRLHF when Requirements: Min 8 GB RAM; Installation requires CUDA version 12.4 and PyTorch v2.6.0, with specific dependencies like vLLM and FlashAttention that are critical.; Tags unique to open-r1: cuda, deepseek-r1, flashattention, model distillation; Use Open-R1 when you need a detailed understanding of how DeepSeek-R1 operates, considering the project closely mirrors its architecture and processes.
- When should I choose OpenRLHF over open-r1?
- Choose OpenRLHF over open-r1 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; 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 open-r1?
- Avoid Open-R1 if your hardware does not support CUDA 12.4 or cannot run PyTorch
v2.6.0, as this may lead to errors. Do not use it if the need for rapid experimentation outweighs the value of detailed replication, since the multi-stage training and datasets curation process can be time-consuming. - 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 open-r1 or OpenRLHF more popular on GitHub?
- open-r1 has more GitHub stars (26,423 vs 9,891). Stars measure visibility, not whether either tool fits your constraints.
- Are open-r1 and OpenRLHF open source?
- Yes - both are open-source projects on GitHub (open-r1: Apache-2.0, OpenRLHF: Apache-2.0).
- Where can I find alternatives to open-r1 or OpenRLHF?
- GraphCanon lists graph-backed alternatives at open-r1 alternatives and OpenRLHF alternatives (open-r1 markdown twin, OpenRLHF markdown twin), 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 mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, open-r1 or OpenRLHF?
- open-r1: Slowing. 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 open-r1 and OpenRLHF?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: open-r1 trust report; OpenRLHF trust report.