Comparison
ml-surveys vs awesome-RLHF
Verdict
Pick ml-surveys if ml-surveys is a collection of detailed review papers summarizing advancements in various AI domains such as deep learning, NLP, CV, graphs, reinforcement learning, and recommendation systems; 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.
Markdown twin · ml-surveys alternatives · awesome-RLHF alternatives
GraphCanon updated 4d
vs
Trust & integrity
| Signal | ml-surveys | awesome-RLHF |
|---|---|---|
| Maintenance | Dormant (1223d since push) As of 1mo · github_public_v1 | Steady (89d since push) As of 4d · github_public_v1 |
| Provenance | Not a fork · Personal account As of 1mo · github_public_v1 | Not a fork · Organization account As of 4d · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) 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
- ml-surveys
- Survey papers summarizing advances in various AI domains
- awesome-RLHF
- A curated list of reinforcement learning with human feedback resources (continually updated)
Stars
- ml-surveys
- 2.9k
- awesome-RLHF
- 4.4k
Forks
- ml-surveys
- 291
- awesome-RLHF
- 258
Open issues
- ml-surveys
- 2
- awesome-RLHF
- 6
Language
- ml-surveys
- -
- awesome-RLHF
- -
Adopt for
- ml-surveys
- ml-surveys is a collection of detailed review papers summarizing advancements in various AI domains such as deep learning, NLP, CV, graphs, reinforcement learning, and recommendation systems.
- 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.
Persona
- ml-surveys
- -
- awesome-RLHF
- -
Runtime
- ml-surveys
- -
- awesome-RLHF
- -
License
- ml-surveys
- MIT
- awesome-RLHF
- Apache-2.0
Last pushed
- ml-surveys
- Mar 17, 2023
- awesome-RLHF
- May 20, 2026
Categories
- ml-surveys
- Computer Vision, Evaluation & Observability, Model Training
- awesome-RLHF
- Evaluation & Observability, Model Training
Trust and health
Maintenance
- ml-surveys
- Dormant (18%)
- awesome-RLHF
- Steady (60%)
Days since push
- ml-surveys
- 1223d
- awesome-RLHF
- 89d
Open issues (now)
- ml-surveys
- 2
- awesome-RLHF
- 6
Stars delta
- ml-surveys
- Unknown
- awesome-RLHF
- +9 (30d)
Open issues delta
- ml-surveys
- Unknown
- awesome-RLHF
- 0 (30d)
Owner type
- ml-surveys
- User
- awesome-RLHF
- Organization
Full report
- ml-surveys
- Trust report
- awesome-RLHF
- Trust report
Choose ml-surveys if…
- License: ml-surveys is MIT, awesome-RLHF is Apache-2.0.
- Tags unique to ml-surveys: computer-vision, embeddings, machine-learning, nlp.
- Also covers Computer Vision.
- When you need comprehensive overviews and summaries of the latest research trends in multiple areas within machine learning
When NOT to use ml-surveys
- If you are seeking detailed technical details, original experiments, or specific algorithm implementations as ml-surveys focuses more on synthesis and summary
- In cases where deep-dive analysis is required into a single niche topic, as ml-surveys provides broad overviews rather than in-depth coverage of individual niches
Choose awesome-RLHF if…
- License: awesome-RLHF is Apache-2.0, ml-surveys is MIT.
- Tags unique to awesome-RLHF: depth-reinforcement-learning, human-feedback, large language models, rlhf.
- 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (eugeneyan/ml-surveys) · observed Jul 22, 2026
- GitHub forks (eugeneyan/ml-surveys) · observed Jul 22, 2026
- Last push (eugeneyan/ml-surveys) · observed Mar 17, 2023
- License file (MIT) · observed Jul 22, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- 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 on cards: ml-surveys 2.9k · awesome-RLHF 4.4k (synced Jul 22, 2026).
Common questions
- What is the difference between ml-surveys and awesome-RLHF?
- ml-surveys: Survey papers summarizing advances in various AI domains. awesome-RLHF: A curated list of reinforcement learning with human feedback resources (continually updated). See the comparison table for live GitHub stats and shared categories.
- When should I choose ml-surveys over awesome-RLHF?
- Choose ml-surveys over awesome-RLHF when License: ml-surveys is MIT, awesome-RLHF is Apache-2.0; Tags unique to ml-surveys: computer-vision, embeddings, machine-learning, nlp; Also covers Computer Vision; When you need comprehensive overviews and summaries of the latest research trends in multiple areas within machine learning.
- When should I choose awesome-RLHF over ml-surveys?
- Choose awesome-RLHF over ml-surveys when License: awesome-RLHF is Apache-2.0, ml-surveys is MIT; Tags unique to awesome-RLHF: depth-reinforcement-learning, human-feedback, large language models, rlhf; 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 avoid ml-surveys?
- If you are seeking detailed technical details, original experiments, or specific algorithm implementations as ml-surveys focuses more on synthesis and summary In cases where deep-dive analysis is required into a single niche topic, as ml-surveys provides broad overviews rather than in-depth coverage of individual niches
- 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.
- Is ml-surveys or awesome-RLHF more popular on GitHub?
- awesome-RLHF has more GitHub stars (4,422 vs 2,902). Stars measure visibility, not whether either tool fits your constraints.
- Are ml-surveys and awesome-RLHF open source?
- Yes - both are open-source projects on GitHub (ml-surveys: MIT, awesome-RLHF: Apache-2.0).
- Where can I find alternatives to ml-surveys or awesome-RLHF?
- GraphCanon lists graph-backed alternatives at ml-surveys alternatives and awesome-RLHF alternatives (ml-surveys markdown twin, awesome-RLHF 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, ml-surveys or awesome-RLHF?
- ml-surveys: Dormant. awesome-RLHF: Steady. 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 ml-surveys and awesome-RLHF?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: ml-surveys trust report; awesome-RLHF trust report.