--- title: "ml-surveys vs awesome-RLHF" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/eugeneyan-ml-surveys-vs-opendilab-awesome-rlhf" tools: ["eugeneyan-ml-surveys", "opendilab-awesome-rlhf"] --- # ml-surveys vs awesome-RLHF *GraphCanon updated Aug 17, 2026* ## 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. [ml-surveys](https://github.com/eugeneyan/ml-surveys) reports 2.9k GitHub stars, 291 forks, and 2 open issues, last pushed Mar 17, 2023. [awesome-RLHF](https://github.com/opendilab/awesome-RLHF) has 4.4k stars, 258 forks, and 6 open issues, last pushed May 20, 2026. Figures are from public GitHub metadata via [ml-surveys's repository](https://github.com/eugeneyan/ml-surveys) and [awesome-RLHF's repository](https://github.com/opendilab/awesome-RLHF). | | [ml-surveys](/tools/eugeneyan-ml-surveys.md) | [awesome-RLHF](/tools/opendilab-awesome-rlhf.md) | | --- | --- | --- | | Tagline | Survey papers summarizing advances in various AI domains | A curated list of reinforcement learning with human feedback resources (continually updated) | | Stars | 2,902 | 4,422 | | Forks | 291 | 258 | | Open issues | 2 | 6 | | Language | - | - | | Adopt for | 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 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 | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | Computer Vision, Evaluation & Observability, Model Training | Evaluation & Observability, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ml-surveys](/tools/eugeneyan-ml-surveys.md) | [awesome-RLHF](/tools/opendilab-awesome-rlhf.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Steady (60%) | | Days since push | 1223d | 89d | | Open issues (now) | 2 | 6 | | Stars delta | Unknown | +9 (30d) | | Open issues delta | Unknown | 0 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/eugeneyan-ml-surveys/trust.md) | [trust report](/tools/opendilab-awesome-rlhf/trust.md) | ## Decision facts: ml-surveys - **Adopt for:** 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. ## Decision facts: awesome-RLHF - **Adopt for:** 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. ## Choose when ### 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 ### 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 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 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. ## 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](/tools/eugeneyan-ml-surveys/alternatives) and [awesome-RLHF alternatives](/tools/opendilab-awesome-rlhf/alternatives) ([ml-surveys markdown twin](/tools/eugeneyan-ml-surveys/alternatives.md), [awesome-RLHF markdown twin](/tools/opendilab-awesome-rlhf/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/eugeneyan-ml-surveys-vs-opendilab-awesome-rlhf.md) 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](/tools/eugeneyan-ml-surveys/trust); [awesome-RLHF trust report](/tools/opendilab-awesome-rlhf/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=eugeneyan-ml-surveys`](/api/graphcanon/graph?tool=eugeneyan-ml-surveys) - 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/_