--- title: "awesome-RLHF vs RLTF" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/opendilab-awesome-rlhf-vs-zyq-scut-rltf" tools: ["opendilab-awesome-rlhf", "zyq-scut-rltf"] --- # awesome-RLHF vs RLTF *GraphCanon updated Aug 17, 2026* ## Verdict 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; pick RLTF if rLTF implements reinforcement learning for text generation using elements from CodeRL, APPS, and transformers. [awesome-RLHF](https://github.com/opendilab/awesome-RLHF) reports 4.4k GitHub stars, 258 forks, and 6 open issues, last pushed May 20, 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-RLHF's repository](https://github.com/opendilab/awesome-RLHF) and [RLTF's repository](https://github.com/Zyq-scut/RLTF). | | [awesome-RLHF](/tools/opendilab-awesome-rlhf.md) | [RLTF](/tools/zyq-scut-rltf.md) | | --- | --- | --- | | Tagline | A curated list of reinforcement learning with human feedback resources (continually updated) | Accepted by Transactions on Machine Learning Research (TMLR) | | Stars | 4,422 | 134 | | Forks | 258 | 7 | | Open issues | 6 | 0 | | Language | - | Python | | 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. | 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-RLHF](/tools/opendilab-awesome-rlhf.md) | [RLTF](/tools/zyq-scut-rltf.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Dormant (18%) | | Days since push | 89d | 669d | | Open issues (now) | 6 | 0 | | Stars delta | +9 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Owner type | Organization | User | | Full report | [trust report](/tools/opendilab-awesome-rlhf/trust.md) | [trust report](/tools/zyq-scut-rltf/trust.md) | ## 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. ## Decision facts: RLTF - **Adopt for:** RLTF implements reinforcement learning for text generation using elements from CodeRL, APPS, and transformers. ## Choose when ### Choose awesome-RLHF if… - License: awesome-RLHF is Apache-2.0, RLTF is BSD-3-Clause. - Tags unique to awesome-RLHF: deep-learning, depth-reinforcement-learning, human-feedback, large language models. - Also covers Evaluation & Observability. - When you are specifically interested in the resources that pertain to enhancing reinforcement learning algorithms with human feedback for developing advanced AI systems. ### Choose RLTF if… - License: RLTF is BSD-3-Clause, awesome-RLHF 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-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. ## 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-RLHF and RLTF? awesome-RLHF: A curated list of reinforcement learning with human feedback resources (continually updated). 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-RLHF over RLTF? Choose awesome-RLHF over RLTF when License: awesome-RLHF is Apache-2.0, RLTF is BSD-3-Clause; Tags unique to awesome-RLHF: deep-learning, depth-reinforcement-learning, human-feedback, large language models; Also covers Evaluation & Observability; 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 choose RLTF over awesome-RLHF? Choose RLTF over awesome-RLHF when License: RLTF is BSD-3-Clause, awesome-RLHF 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-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. ### 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-RLHF or RLTF more popular on GitHub? awesome-RLHF has more GitHub stars (4,422 vs 134). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-RLHF and RLTF open source? Yes - both are open-source projects on GitHub (awesome-RLHF: Apache-2.0, RLTF: BSD-3-Clause). ### Where can I find alternatives to awesome-RLHF or RLTF? GraphCanon lists graph-backed alternatives at [awesome-RLHF alternatives](/tools/opendilab-awesome-rlhf/alternatives) and [RLTF alternatives](/tools/zyq-scut-rltf/alternatives) ([awesome-RLHF markdown twin](/tools/opendilab-awesome-rlhf/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/opendilab-awesome-rlhf-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-RLHF or RLTF? awesome-RLHF: Steady. 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-RLHF and RLTF? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-RLHF trust report](/tools/opendilab-awesome-rlhf/trust); [RLTF trust report](/tools/zyq-scut-rltf/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=opendilab-awesome-rlhf`](/api/graphcanon/graph?tool=opendilab-awesome-rlhf) - 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/_