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 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.
Markdown twin · awesome-RLHF alternatives · OpenRLHF alternatives
GraphCanon updated 3d
vs
Trust & integrity
| Signal | awesome-RLHF | OpenRLHF |
|---|---|---|
| Maintenance | Steady (89d since push) As of 3d · github_public_v1 | Active (24d since push) As of 1w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3d · 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
- 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
Stars
- awesome-RLHF
- 4.4k
- OpenRLHF
- 9.9k
Forks
- awesome-RLHF
- 258
- OpenRLHF
- 996
Open issues
- awesome-RLHF
- 6
- OpenRLHF
- 367
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 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
- 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
- Jul 14, 2026
Categories
- awesome-RLHF
- Evaluation & Observability, Model Training
- OpenRLHF
- Inference & Serving, Model Training
Trust and health
Maintenance
- awesome-RLHF
- Steady (60%)
- OpenRLHF
- Active (82%)
Days since push
- awesome-RLHF
- 89d
- OpenRLHF
- 24d
Open issues (now)
- awesome-RLHF
- 6
- OpenRLHF
- 367
Stars delta
- awesome-RLHF
- +9 (30d)
- OpenRLHF
- +132 (30d)
Open issues delta
- awesome-RLHF
- 0 (30d)
- OpenRLHF
- +25 (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…
- 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 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 (opendilab/awesome-RLHF) · observed Aug 17, 2026
- GitHub forks (opendilab/awesome-RLHF) · observed Aug 17, 2026
- Last push (opendilab/awesome-RLHF) · observed May 20, 2026
- License file (Apache-2.0) · observed Aug 17, 2026
- Decision facts (enrichment) · observed Jul 12, 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: awesome-RLHF 4.4k · OpenRLHF 9.9k (synced Aug 17, 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: 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 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: 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.