--- title: "awesome-automl-papers vs RLTF" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hibayesian-awesome-automl-papers-vs-zyq-scut-rltf" tools: ["hibayesian-awesome-automl-papers", "zyq-scut-rltf"] --- # awesome-automl-papers vs RLTF *GraphCanon updated Aug 5, 2026* ## 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 RLTF if rLTF implements reinforcement learning for text generation using elements from CodeRL, APPS, and transformers. [awesome-automl-papers](https://github.com/hibayesian/awesome-automl-papers) reports 4.2k GitHub stars, 678 forks, and 2 open issues, last pushed Jun 11, 2024. [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-papers's repository](https://github.com/hibayesian/awesome-automl-papers) and [RLTF's repository](https://github.com/Zyq-scut/RLTF). | | [awesome-automl-papers](/tools/hibayesian-awesome-automl-papers.md) | [RLTF](/tools/zyq-scut-rltf.md) | | --- | --- | --- | | Tagline | A curated list of automated machine learning papers and resources. | Accepted by Transactions on Machine Learning Research (TMLR) | | Stars | 4,155 | 134 | | Forks | 678 | 7 | | Open issues | 2 | 0 | | Language | - | Python | | Adopt for | awesome-automl-papers is an organized collection of AutoML academic resources including papers on automated feature engineering, hyperparameter optimization, and neural architecture search. | RLTF implements reinforcement learning for text generation using elements from CodeRL, APPS, and transformers. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | BSD-3-Clause | | Categories | Evaluation & Observability, Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-automl-papers](/tools/hibayesian-awesome-automl-papers.md) | [RLTF](/tools/zyq-scut-rltf.md) | | --- | --- | --- | | Days since push | 784d | 669d | | Open issues (now) | 2 | 0 | | Full report | [trust report](/tools/hibayesian-awesome-automl-papers/trust.md) | [trust report](/tools/zyq-scut-rltf/trust.md) | ## Decision facts: awesome-automl-papers - **Adopt for:** awesome-automl-papers is an organized collection of AutoML academic resources including papers on automated feature engineering, hyperparameter optimization, and neural architecture search. ## Decision facts: RLTF - **Adopt for:** RLTF implements reinforcement learning for text generation using elements from CodeRL, APPS, and transformers. ## Choose when ### Choose awesome-automl-papers if… - License: awesome-automl-papers is Apache-2.0, RLTF is BSD-3-Clause. - Tags unique to awesome-automl-papers: automl, feature-engineering, hyperparameter-optimization, neural-architecture-search. - Also covers Evaluation & Observability. - When you need a curated list of academic materials to research or learn about AutoML technologies ### Choose RLTF if… - License: RLTF is BSD-3-Clause, awesome-automl-papers is Apache-2.0. - 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-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 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-papers and RLTF? awesome-automl-papers: A curated list of automated machine learning papers 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-papers over RLTF? Choose awesome-automl-papers over RLTF when License: awesome-automl-papers is Apache-2.0, RLTF is BSD-3-Clause; Tags unique to awesome-automl-papers: automl, feature-engineering, hyperparameter-optimization, neural-architecture-search; Also covers Evaluation & Observability; When you need a curated list of academic materials to research or learn about AutoML technologies. ### When should I choose RLTF over awesome-automl-papers? Choose RLTF over awesome-automl-papers when License: RLTF is BSD-3-Clause, awesome-automl-papers is Apache-2.0; 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-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 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-papers or RLTF more popular on GitHub? awesome-automl-papers has more GitHub stars (4,155 vs 134). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-automl-papers and RLTF open source? Yes - both are open-source projects on GitHub (awesome-automl-papers: Apache-2.0, RLTF: BSD-3-Clause). ### Where can I find alternatives to awesome-automl-papers or RLTF? GraphCanon lists graph-backed alternatives at [awesome-automl-papers alternatives](/tools/hibayesian-awesome-automl-papers/alternatives) and [RLTF alternatives](/tools/zyq-scut-rltf/alternatives) ([awesome-automl-papers markdown twin](/tools/hibayesian-awesome-automl-papers/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/hibayesian-awesome-automl-papers-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-papers or RLTF? awesome-automl-papers: Dormant. 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-papers and RLTF? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-automl-papers trust report](/tools/hibayesian-awesome-automl-papers/trust); [RLTF trust report](/tools/zyq-scut-rltf/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=hibayesian-awesome-automl-papers`](/api/graphcanon/graph?tool=hibayesian-awesome-automl-papers) - 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/_