--- title: "awesome-AutoML vs RLTF" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/windmaple-awesome-automl-vs-zyq-scut-rltf" tools: ["windmaple-awesome-automl", "zyq-scut-rltf"] --- # awesome-AutoML vs RLTF *GraphCanon updated Aug 5, 2026* ## 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. [awesome-AutoML](https://github.com/windmaple/awesome-AutoML) reports 941 GitHub stars, 156 forks, and 1 open issues, last pushed Mar 24, 2026. [RLTF](https://github.com/Zyq-scut/RLTF) has 134 stars, 7 forks, and 0 open issues, last pushed Oct 5, 2024. Figures are from public GitHub metadata via [awesome-AutoML's repository](https://github.com/windmaple/awesome-AutoML) and [RLTF's repository](https://github.com/Zyq-scut/RLTF). | | [awesome-AutoML](/tools/windmaple-awesome-automl.md) | [RLTF](/tools/zyq-scut-rltf.md) | | --- | --- | --- | | Tagline | Curating AutoML research and resources | Accepted by Transactions on Machine Learning Research (TMLR) | | Stars | 941 | 134 | | Forks | 156 | 7 | | Open issues | 1 | 0 | | Language | - | Python | | Adopt for | Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. | RLTF implements reinforcement learning for text generation using elements from CodeRL, APPS, and transformers. | | Persona | - | - | | Runtime | - | - | | License | GPL-3.0 | BSD-3-Clause | | Categories | Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-AutoML](/tools/windmaple-awesome-automl.md) | [RLTF](/tools/zyq-scut-rltf.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 133d | 669d | | Open issues (now) | 1 | 0 | | Full report | [trust report](/tools/windmaple-awesome-automl/trust.md) | [trust report](/tools/zyq-scut-rltf/trust.md) | ## Decision facts: awesome-AutoML - **Adopt for:** Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. ## Decision facts: RLTF - **Adopt for:** RLTF implements reinforcement learning for text generation using elements from CodeRL, APPS, and transformers. ## Choose when ### 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. ### 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 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 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. ## 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](/tools/windmaple-awesome-automl/alternatives) and [RLTF alternatives](/tools/zyq-scut-rltf/alternatives) ([awesome-AutoML markdown twin](/tools/windmaple-awesome-automl/alternatives.md), [RLTF markdown twin](/tools/zyq-scut-rltf/alternatives.md)), 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](/compare/windmaple-awesome-automl-vs-zyq-scut-rltf.md) 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](/tools/windmaple-awesome-automl/trust); [RLTF trust report](/tools/zyq-scut-rltf/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=windmaple-awesome-automl`](/api/graphcanon/graph?tool=windmaple-awesome-automl) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_