Comparison
awesome-automl-papers vs awesome-RLHF
Verdict
Pick awesome-automl-papers if awesome-automl-papers is an organized collection of AutoML academic resources including papers on automated feature engineering, hyperparameter optimization, and neural architecture search; 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 · awesome-automl-papers alternatives · awesome-RLHF alternatives
GraphCanon updated 4d
vs
Trust & integrity
| Signal | awesome-automl-papers | awesome-RLHF |
|---|---|---|
| Maintenance | Dormant (784d since push) As of 2w · github_public_v1 | Steady (89d since push) As of 4d · github_public_v1 |
| Provenance | Not a fork · Personal account As of 2w · 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
- awesome-automl-papers
- A curated list of automated machine learning papers and resources.
- awesome-RLHF
- A curated list of reinforcement learning with human feedback resources (continually updated)
Stars
- awesome-automl-papers
- 4.2k
- awesome-RLHF
- 4.4k
Forks
- awesome-automl-papers
- 678
- awesome-RLHF
- 258
Open issues
- awesome-automl-papers
- 2
- awesome-RLHF
- 6
Language
- awesome-automl-papers
- -
- awesome-RLHF
- -
Adopt for
- awesome-automl-papers
- awesome-automl-papers is an organized collection of AutoML academic resources including papers on automated feature engineering, hyperparameter optimization, and neural architecture search.
- 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
- awesome-automl-papers
- -
- awesome-RLHF
- -
Runtime
- awesome-automl-papers
- -
- awesome-RLHF
- -
License
- awesome-automl-papers
- Apache-2.0
- awesome-RLHF
- Apache-2.0
Last pushed
- awesome-automl-papers
- Jun 11, 2024
- awesome-RLHF
- May 20, 2026
Categories
- awesome-automl-papers
- Evaluation & Observability, Model Training
- awesome-RLHF
- Evaluation & Observability, Model Training
Trust and health
Maintenance
- awesome-automl-papers
- Dormant (18%)
- awesome-RLHF
- Steady (60%)
Days since push
- awesome-automl-papers
- 784d
- awesome-RLHF
- 89d
Open issues (now)
- awesome-automl-papers
- 2
- awesome-RLHF
- 6
Stars delta
- awesome-automl-papers
- Unknown
- awesome-RLHF
- +9 (30d)
Open issues delta
- awesome-automl-papers
- Unknown
- awesome-RLHF
- 0 (30d)
Owner type
- awesome-automl-papers
- User
- awesome-RLHF
- Organization
Full report
- awesome-automl-papers
- Trust report
- awesome-RLHF
- Trust report
Choose awesome-automl-papers if…
- Tags unique to awesome-automl-papers: automl, feature-engineering, hyperparameter-optimization, neural-architecture-search.
- When you need a curated list of academic materials to research or learn about AutoML technologies
- Leaner open-issue backlog (2).
When NOT to use awesome-automl-papers
- If looking for direct integration with commercial AutoML systems, as the tool provides only a list of academic papers and resources
- When seeking practical AutoML solutions to directly apply in production settings without extensive customization or interpretation from papers
Choose awesome-RLHF if…
- Tags unique to awesome-RLHF: deep-learning, depth-reinforcement-learning, human-feedback, large language models.
- When you are specifically interested in the resources that pertain to enhancing reinforcement learning algorithms with human feedback for developing advanced AI systems.
- More GitHub stars (4.4k vs 4.2k) - visibility, not fit.
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 (hibayesian/awesome-automl-papers) · observed Aug 4, 2026
- GitHub forks (hibayesian/awesome-automl-papers) · observed Aug 4, 2026
- Last push (hibayesian/awesome-automl-papers) · observed Jun 11, 2024
- License file (Apache-2.0) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 15, 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: awesome-automl-papers 4.2k · awesome-RLHF 4.4k (synced Aug 4, 2026).
Common questions
- What is the difference between awesome-automl-papers and awesome-RLHF?
- awesome-automl-papers: A curated list of automated machine learning papers and resources.. 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 awesome-automl-papers over awesome-RLHF?
- Choose awesome-automl-papers over awesome-RLHF when Tags unique to awesome-automl-papers: automl, feature-engineering, hyperparameter-optimization, neural-architecture-search; When you need a curated list of academic materials to research or learn about AutoML technologies; Leaner open-issue backlog (2).
- When should I choose awesome-RLHF over awesome-automl-papers?
- Choose awesome-RLHF over awesome-automl-papers when Tags unique to awesome-RLHF: deep-learning, depth-reinforcement-learning, human-feedback, large language models; When you are specifically interested in the resources that pertain to enhancing reinforcement learning algorithms with human feedback for developing advanced AI systems; More GitHub stars (4.4k vs 4.2k) - visibility, not fit.
- When should I avoid awesome-automl-papers?
- If looking for direct integration with commercial AutoML systems, as the tool provides only a list of academic papers and resources When seeking practical AutoML solutions to directly apply in production settings without extensive customization or interpretation from papers
- 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 awesome-automl-papers or awesome-RLHF more popular on GitHub?
- awesome-RLHF has more GitHub stars (4,422 vs 4,155). Stars measure visibility, not whether either tool fits your constraints.
- Are awesome-automl-papers and awesome-RLHF open source?
- Yes - both are open-source projects on GitHub (awesome-automl-papers: Apache-2.0, awesome-RLHF: Apache-2.0).
- Where can I find alternatives to awesome-automl-papers or awesome-RLHF?
- GraphCanon lists graph-backed alternatives at awesome-automl-papers alternatives and awesome-RLHF alternatives (awesome-automl-papers 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, awesome-automl-papers or awesome-RLHF?
- awesome-automl-papers: 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 awesome-automl-papers and awesome-RLHF?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-automl-papers trust report; awesome-RLHF trust report.