Comparison
awesome-RLHF vs OpenRLHF
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 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+.
Markdown twin · awesome-RLHF alternatives · OpenRLHF alternatives
GraphCanon updated Sep 20, 2026
13views this month
vs
Trust & integrity
| Signal | awesome-RLHF | OpenRLHF |
|---|---|---|
| Maintenance | Steady (89d since push) As of Aug 17, 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 Aug 17, 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 |
Tagline
- awesome-RLHF
- A curated list of reinforcement learning with human feedback resources (continually updated)
- OpenRLHF
- Scalable and High-performance Agentic RL Framework based on Ray
Stars
- awesome-RLHF
- 4.4k
- OpenRLHF
- 10k
Forks
- awesome-RLHF
- 258
- OpenRLHF
- 1.0k
Open issues
- awesome-RLHF
- 6
- OpenRLHF
- 380
Language
- awesome-RLHF
- -
- OpenRLHF
- Python
Adopt for
- awesome-RLHF
- 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
- 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
- awesome-RLHF
- -
- OpenRLHF
- -
Runtime
- awesome-RLHF
- -
- OpenRLHF
- -
License
- awesome-RLHF
- Apache-2.0
- OpenRLHF
- Apache-2.0
Last pushed
- awesome-RLHF
- May 20, 2026
- OpenRLHF
- Sep 17, 2026
Categories
- awesome-RLHF
- Evaluation & Observability, Model Training
- OpenRLHF
- Inference & Serving, Model Training
Trust and health
Maintenance
- awesome-RLHF
- Steady (60%)
- OpenRLHF
- Very active (96%)
Days since push
- awesome-RLHF
- 89d
- OpenRLHF
- 1d
Open issues (now)
- awesome-RLHF
- 6
- OpenRLHF
- 380
Stars delta
- awesome-RLHF
- +9 (30d)
- OpenRLHF
- +123 (30d)
Open issues delta
- awesome-RLHF
- 0 (30d)
- OpenRLHF
- +13 (30d)
OSV dependency advisories
- awesome-RLHF
- No lockfile (source not queried)
- OpenRLHF
- Published findings
Full report
- awesome-RLHF
- Trust report
- OpenRLHF
- Trust report
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.
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.
Choose OpenRLHF if…
- Tags unique to OpenRLHF: proximal-policy-optimization, raylib, reinforcement-learning-from-human-feedba, transformers.
- 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (opendilab/awesome-RLHF) · observed Sep 20, 2026
- GitHub forks (opendilab/awesome-RLHF) · observed Sep 20, 2026
- Last push (opendilab/awesome-RLHF) · observed May 20, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- 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 on cards: awesome-RLHF 4.4k · OpenRLHF 10k (synced Sep 20, 2026).
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: 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 Tags unique to OpenRLHF: proximal-policy-optimization, raylib, reinforcement-learning-from-human-feedba, transformers; 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 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 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 awesome-RLHF or OpenRLHF more popular on GitHub?
- OpenRLHF has more GitHub stars (10,014 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 and OpenRLHF alternatives (awesome-RLHF 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, awesome-RLHF or OpenRLHF?
- awesome-RLHF: Steady. 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 awesome-RLHF and OpenRLHF?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-RLHF trust report; OpenRLHF trust report.