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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 scalable and high-performance reinforcement learning framework from human feedback, built on Ray for distributed scheduling and vLLM for efficient inference, supporting models up to 70B+ parameters.

Markdown twin · open-r1 alternatives · OpenRLHF alternatives

GraphCanon updated Oct 10, 2026

12views this month

open-r1 logo

open-r1

huggingface/open-r1

★ 26kpushed Oct 2, 2026
vs
OpenRLHF logo

OpenRLHF

OpenRLHF/OpenRLHF

★ 10kpushed Sep 17, 2026

Trust & integrity

Signalopen-r1OpenRLHF
Maintenance
Active (8d since push)
As of Oct 10, 2026 · github_public_v1
Very active (1d since push)
As of Sep 18, 2026 · github_public_v1
Provenance
Not a fork · Organization account
As of Oct 10, 2026 · github_public_v1
Not a fork · Organization account
As of Sep 18, 2026 · github_public_v1
OSV dependency advisories
No lockfile (source not queried)
As of Jul 11, 2026 · osv@v1
Published findings
As of Sep 18, 2026 · 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
open-r1 trust reportOpenRLHF trust reportTrust methodology

Tagline

open-r1
Fully open reproduction of DeepSeek-R1
OpenRLHF
Scalable and High-performance Agentic RL Framework based on Ray

Stars

open-r1
26k
OpenRLHF
10k

Forks

open-r1
2.5k
OpenRLHF
1.0k

Open issues

open-r1
0
OpenRLHF
380

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 scalable and high-performance reinforcement learning framework from human feedback, built on Ray for distributed scheduling and vLLM for efficient inference, supporting models up to 70B+ parameters.

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
Oct 2, 2026
OpenRLHF
Sep 17, 2026

Categories

open-r1
Inference & Serving, Model Training
OpenRLHF
Inference & Serving, Model Training

Trust and health

Maintenance

open-r1
Active (82%)
OpenRLHF
Very active (96%)

Days since push

open-r1
8d
OpenRLHF
1d

Open issues (now)

open-r1
0
OpenRLHF
380

Stars delta

open-r1
+28 (30d)
OpenRLHF
+123 (30d)

Open issues delta

open-r1
-340 (30d)
OpenRLHF
+13 (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.
GitHubDetails

Choose OpenRLHF if…

  • Tags unique to OpenRLHF: large-language-models, proximal-policy-optimization, raylib, reinforcement learning.
  • When you need to train large models up to 70B+ parameters with efficient distributed scheduling and high-performance inference.

When NOT to use OpenRLHF

  • If your project does not require support for large models or distributed training, as OpenRLHF's complexity and resource requirements may be unnecessary.
  • When you prefer a framework that does not rely on Ray for distributed scheduling, as OpenRLHF's architecture is tightly coupled with Ray for its distributed capabilities.
GitHubDetails

Explore

open-r1 trust report →OpenRLHF trust report →Inference & Serving category →Model Training category →All comparisonsStack workflowsTrending tools

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: open-r1 26k · OpenRLHF 10k (synced Oct 10, 2026).

Common questions

What is the difference between open-r1 and OpenRLHF?
open-r1: Fully open reproduction of DeepSeek-R1. OpenRLHF: 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 Tags unique to OpenRLHF: large-language-models, proximal-policy-optimization, raylib, reinforcement learning; When you need to train large models up to 70B+ parameters with efficient distributed scheduling and high-performance inference.
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 support for large models or distributed training, as OpenRLHF's complexity and resource requirements may be unnecessary. When you prefer a framework that does not rely on Ray for distributed scheduling, as OpenRLHF's architecture is tightly coupled with Ray for its distributed capabilities.
Is open-r1 or OpenRLHF more popular on GitHub?
open-r1 has more GitHub stars (26,477 vs 10,014). 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: Active. OpenRLHF: 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 open-r1 and OpenRLHF?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: open-r1 trust report; OpenRLHF trust report.

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