Comparison
OpenRLHF vs CodeRL
Verdict
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; pick CodeRL if codeRL is an advanced tool that uses pretrained models and deep reinforcement learning to generate code.
Markdown twin · OpenRLHF alternatives · CodeRL alternatives
GraphCanon updated Oct 7, 2026
8views this month
Trust & integrity
| Signal | OpenRLHF | CodeRL |
|---|---|---|
| Maintenance | Very active (1d since push) As of Sep 18, 2026 · github_public_v1 | Slowing (126d since push) As of Oct 7, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 18, 2026 · github_public_v1 | Not a fork · Organization account As of Oct 7, 2026 · github_public_v1 |
| OSV dependency advisories | Published findings As of Sep 18, 2026 · osv@v1 | Published findings As of Jul 11, 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 |
Tagline
- OpenRLHF
- Scalable and High-performance Agentic RL Framework based on Ray
- CodeRL
- CodeRL: Combines pretrained models and reinforcement learning for code generation.
Stars
- OpenRLHF
- 10k
- CodeRL
- 576
Forks
- OpenRLHF
- 1.0k
- CodeRL
- 69
Open issues
- OpenRLHF
- 380
- CodeRL
- 42
Language
- OpenRLHF
- Python
- CodeRL
- Python
Adopt for
- 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.
- CodeRL
- CodeRL is an advanced tool that uses pretrained models and deep reinforcement learning to generate code.
Persona
- OpenRLHF
- -
- CodeRL
- -
Runtime
- OpenRLHF
- -
- CodeRL
- -
License
- OpenRLHF
- Apache-2.0
- CodeRL
- BSD-3-Clause
Last pushed
- OpenRLHF
- Sep 17, 2026
- CodeRL
- Jun 2, 2026
Categories
- OpenRLHF
- Inference & Serving, Model Training
- CodeRL
- Developer Tools, Model Training
Trust and health
Maintenance
- OpenRLHF
- Very active (96%)
- CodeRL
- Slowing (36%)
Days since push
- OpenRLHF
- 1d
- CodeRL
- 126d
Open issues (now)
- OpenRLHF
- 380
- CodeRL
- 42
Stars delta
- OpenRLHF
- +123 (30d)
- CodeRL
- +1 (30d)
Open issues delta
- OpenRLHF
- +13 (30d)
- CodeRL
- 0 (30d)
Full report
- OpenRLHF
- Trust report
- CodeRL
- Trust report
Shared compatibility
- Python · OpenRLHF: Python runtime · CodeRL: Python runtime
Choose OpenRLHF if…
- License: OpenRLHF is Apache-2.0, CodeRL is BSD-3-Clause.
- Tags unique to OpenRLHF: large-language-models, proximal-policy-optimization, raylib, reinforcement learning.
- Also covers Inference & Serving.
- 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.
Choose CodeRL if…
- License: CodeRL is BSD-3-Clause, OpenRLHF is Apache-2.0.
- Tags unique to CodeRL: ai, codegeneration, languagemodel, machinelearning.
- Also covers Developer Tools.
- When you need to generate complex and context-aware code snippets utilizing the latest in reinforcement learning techniques.
When NOT to use CodeRL
- Avoid if your project requires only simple, quick code generation without deep reinforcement learning support.
- Do not use if compatibility with versions of the Hugging Face transformers library other than 4.16.1 is critical to avoid potential issues.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (OpenRLHF/OpenRLHF) · observed Sep 20, 2026
- GitHub forks (OpenRLHF/OpenRLHF) · observed Sep 20, 2026
- Last push (OpenRLHF/OpenRLHF) · observed Sep 17, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
- GitHub stars (salesforce/CodeRL) · observed Oct 7, 2026
- GitHub forks (salesforce/CodeRL) · observed Oct 7, 2026
- Last push (salesforce/CodeRL) · observed Jun 2, 2026
- License file (BSD-3-Clause) · observed Oct 7, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: OpenRLHF 10k · CodeRL 576 (synced Sep 20, 2026).
Common questions
- What is the difference between OpenRLHF and CodeRL?
- OpenRLHF: Scalable and High-performance Agentic RL Framework based on Ray. CodeRL: CodeRL: Combines pretrained models and reinforcement learning for code generation.. See the comparison table for live GitHub stats and shared categories.
- When should I choose OpenRLHF over CodeRL?
- Choose OpenRLHF over CodeRL when License: OpenRLHF is Apache-2.0, CodeRL is BSD-3-Clause; Tags unique to OpenRLHF: large-language-models, proximal-policy-optimization, raylib, reinforcement learning; Also covers Inference & Serving; When you need to train large models up to 70B+ parameters with efficient distributed scheduling and high-performance inference.
- When should I choose CodeRL over OpenRLHF?
- Choose CodeRL over OpenRLHF when License: CodeRL is BSD-3-Clause, OpenRLHF is Apache-2.0; Tags unique to CodeRL: ai, codegeneration, languagemodel, machinelearning; Also covers Developer Tools; When you need to generate complex and context-aware code snippets utilizing the latest in reinforcement learning techniques.
- 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.
- When should I avoid CodeRL?
- Avoid if your project requires only simple, quick code generation without deep reinforcement learning support. Do not use if compatibility with versions of the Hugging Face transformers library other than 4.16.1 is critical to avoid potential issues.
- Is OpenRLHF or CodeRL more popular on GitHub?
- OpenRLHF has more GitHub stars (10,014 vs 576). Stars measure visibility, not whether either tool fits your constraints.
- Are OpenRLHF and CodeRL open source?
- Yes - both are open-source projects on GitHub (OpenRLHF: Apache-2.0, CodeRL: BSD-3-Clause).
- Where can I find alternatives to OpenRLHF or CodeRL?
- GraphCanon lists graph-backed alternatives at OpenRLHF alternatives and CodeRL alternatives (OpenRLHF markdown twin, CodeRL 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, OpenRLHF or CodeRL?
- OpenRLHF: Very active. CodeRL: Slowing. 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 OpenRLHF and CodeRL?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: OpenRLHF trust report; CodeRL trust report.