Comparison
awesome-AutoML vs RLTF
Verdict
Pick awesome-AutoML if curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning; pick RLTF if rLTF implements reinforcement learning for text generation using elements from CodeRL, APPS, and transformers.
Markdown twin · awesome-AutoML alternatives · RLTF alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | awesome-AutoML | RLTF |
|---|---|---|
| Maintenance | Slowing (133d since push) As of 2w · github_public_v1 | Dormant (669d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 2w · github_public_v1 | Not a fork · Personal account As of 2w · 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-AutoML
- Curating AutoML research and resources
- RLTF
- Accepted by Transactions on Machine Learning Research (TMLR)
Stars
- awesome-AutoML
- 941
- RLTF
- 134
Forks
- awesome-AutoML
- 156
- RLTF
- 7
Open issues
- awesome-AutoML
- 1
- RLTF
- 0
Language
- awesome-AutoML
- -
- RLTF
- Python
Adopt for
- awesome-AutoML
- Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.
- RLTF
- RLTF implements reinforcement learning for text generation using elements from CodeRL, APPS, and transformers.
Persona
- awesome-AutoML
- -
- RLTF
- -
Runtime
- awesome-AutoML
- -
- RLTF
- -
License
- awesome-AutoML
- GPL-3.0
- RLTF
- BSD-3-Clause
Last pushed
- awesome-AutoML
- Mar 24, 2026
- RLTF
- Oct 5, 2024
Categories
- awesome-AutoML
- Model Training
- RLTF
- Model Training
Trust and health
Maintenance
- awesome-AutoML
- Slowing (36%)
- RLTF
- Dormant (18%)
Days since push
- awesome-AutoML
- 133d
- RLTF
- 669d
Open issues (now)
- awesome-AutoML
- 1
- RLTF
- 0
OSV dependency advisories
- awesome-AutoML
- No lockfile (source not queried)
- RLTF
- Published findings
Full report
- awesome-AutoML
- Trust report
- RLTF
- Trust report
Choose awesome-AutoML if…
- License: awesome-AutoML is GPL-3.0, RLTF is BSD-3-Clause.
- Tags unique to awesome-AutoML: automl, hyperparameter-optimization, meta-learning, neural-architecture-search.
- When seeking comprehensive resources on diverse AutoML topics from recent and impactful research.
When NOT to use awesome-AutoML
- If looking for direct implementation advice as the repository focuses more on linking to resources rather than providing specific how-to guides.
- When requiring real-time or interactive AutoML features, since it's a curation hub rather than an application tool.
Choose RLTF if…
- License: RLTF is BSD-3-Clause, awesome-AutoML is GPL-3.0.
- Tags unique to RLTF: apps, bsd-license, code-rl, open-source-contributions.
- Use RLTF when you need advanced Reinforcement Learning models specifically tuned for text generation tasks.
When NOT to use RLTF
- Avoid RLTF if your project does not require reinforcement learning techniques, especially focused on text generation.
- Do not use this tool if your work is incompatible with components from CodeRL, APPS, or transformers.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (windmaple/awesome-AutoML) · observed Aug 4, 2026
- GitHub forks (windmaple/awesome-AutoML) · observed Aug 4, 2026
- Last push (windmaple/awesome-AutoML) · observed Mar 24, 2026
- License file (GPL-3.0) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (Zyq-scut/RLTF) · observed Aug 5, 2026
- GitHub forks (Zyq-scut/RLTF) · observed Aug 5, 2026
- Last push (Zyq-scut/RLTF) · observed Oct 5, 2024
- License file (BSD-3-Clause) · observed Aug 5, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: awesome-AutoML 941 · RLTF 134 (synced Aug 4, 2026).
Common questions
- What is the difference between awesome-AutoML and RLTF?
- awesome-AutoML: Curating AutoML research and resources. RLTF: Accepted by Transactions on Machine Learning Research (TMLR). See the comparison table for live GitHub stats and shared categories.
- When should I choose awesome-AutoML over RLTF?
- Choose awesome-AutoML over RLTF when License: awesome-AutoML is GPL-3.0, RLTF is BSD-3-Clause; Tags unique to awesome-AutoML: automl, hyperparameter-optimization, meta-learning, neural-architecture-search; When seeking comprehensive resources on diverse AutoML topics from recent and impactful research.
- When should I choose RLTF over awesome-AutoML?
- Choose RLTF over awesome-AutoML when License: RLTF is BSD-3-Clause, awesome-AutoML is GPL-3.0; Tags unique to RLTF: apps, bsd-license, code-rl, open-source-contributions; Use RLTF when you need advanced Reinforcement Learning models specifically tuned for text generation tasks.
- When should I avoid awesome-AutoML?
- If looking for direct implementation advice as the repository focuses more on linking to resources rather than providing specific how-to guides. When requiring real-time or interactive AutoML features, since it's a curation hub rather than an application tool.
- When should I avoid RLTF?
- Avoid RLTF if your project does not require reinforcement learning techniques, especially focused on text generation. Do not use this tool if your work is incompatible with components from CodeRL, APPS, or transformers.
- Is awesome-AutoML or RLTF more popular on GitHub?
- awesome-AutoML has more GitHub stars (941 vs 134). Stars measure visibility, not whether either tool fits your constraints.
- Are awesome-AutoML and RLTF open source?
- Yes - both are open-source projects on GitHub (awesome-AutoML: GPL-3.0, RLTF: BSD-3-Clause).
- Where can I find alternatives to awesome-AutoML or RLTF?
- GraphCanon lists graph-backed alternatives at awesome-AutoML alternatives and RLTF alternatives (awesome-AutoML markdown twin, RLTF 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 or RLTF?
- awesome-AutoML: Slowing. RLTF: Dormant. 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 and RLTF?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-AutoML trust report; RLTF trust report.