--- title: "awesome-RLHF vs CodeRL" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/opendilab-awesome-rlhf-vs-salesforce-coderl" tools: ["opendilab-awesome-rlhf", "salesforce-coderl"] --- # awesome-RLHF vs CodeRL *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 CodeRL if codeRL is an advanced tool that uses pretrained models and deep reinforcement learning to generate code. [awesome-RLHF](https://github.com/opendilab/awesome-RLHF) reports 4.4k GitHub stars, 258 forks, and 6 open issues, last pushed May 20, 2026. [CodeRL](https://github.com/salesforce/CodeRL) has 574 stars, 69 forks, and 42 open issues, last pushed Jun 2, 2026. Figures are from public GitHub metadata via [awesome-RLHF's repository](https://github.com/opendilab/awesome-RLHF) and [CodeRL's repository](https://github.com/salesforce/CodeRL). | | [awesome-RLHF](/tools/opendilab-awesome-rlhf.md) | [CodeRL](/tools/salesforce-coderl.md) | | --- | --- | --- | | Tagline | A curated list of reinforcement learning with human feedback resources (continually updated) | CodeRL: Combines pretrained models and reinforcement learning for code generation. | | Stars | 4,422 | 574 | | Forks | 258 | 69 | | Open issues | 6 | 42 | | 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. | CodeRL is an advanced tool that uses pretrained models and deep reinforcement learning to generate code. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | BSD-3-Clause | | Categories | Evaluation & Observability, Model Training | Developer Tools, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-RLHF](/tools/opendilab-awesome-rlhf.md) | [CodeRL](/tools/salesforce-coderl.md) | | --- | --- | --- | | Days since push | 89d | 63d | | Open issues (now) | 6 | 42 | | Stars delta | +9 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Full report | [trust report](/tools/opendilab-awesome-rlhf/trust.md) | [trust report](/tools/salesforce-coderl/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: CodeRL - **Adopt for:** CodeRL is an advanced tool that uses pretrained models and deep reinforcement learning to generate code. ## Choose when ### Choose awesome-RLHF if… - License: awesome-RLHF is Apache-2.0, CodeRL 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 CodeRL if… - License: CodeRL is BSD-3-Clause, awesome-RLHF is Apache-2.0. - Tags unique to CodeRL: ai, codegeneration, languagemodel, machinelearning. - Also covers Developer Tools. - When you need to generate complex and context-aware code snippets utilizing the latest in reinforcement learning techniques. ## 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 CodeRL - Avoid if your project requires only simple, quick code generation without deep reinforcement learning support. - Do not use if compatibility with versions of the Hugging Face transformers library other than 4.16.1 is critical to avoid potential issues. ## Common questions ### What is the difference between awesome-RLHF and CodeRL? awesome-RLHF: A curated list of reinforcement learning with human feedback resources (continually updated). CodeRL: CodeRL: Combines pretrained models and reinforcement learning for code generation.. See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome-RLHF over CodeRL? Choose awesome-RLHF over CodeRL when License: awesome-RLHF is Apache-2.0, CodeRL 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 CodeRL over awesome-RLHF? Choose CodeRL over awesome-RLHF when License: CodeRL is BSD-3-Clause, awesome-RLHF is Apache-2.0; Tags unique to CodeRL: ai, codegeneration, languagemodel, machinelearning; Also covers Developer Tools; When you need to generate complex and context-aware code snippets utilizing the latest in reinforcement learning techniques. ### 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 CodeRL? Avoid if your project requires only simple, quick code generation without deep reinforcement learning support. Do not use if compatibility with versions of the Hugging Face transformers library other than 4.16.1 is critical to avoid potential issues. ### Is awesome-RLHF or CodeRL more popular on GitHub? awesome-RLHF has more GitHub stars (4,422 vs 574). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-RLHF and CodeRL open source? Yes - both are open-source projects on GitHub (awesome-RLHF: Apache-2.0, CodeRL: BSD-3-Clause). ### Where can I find alternatives to awesome-RLHF or CodeRL? GraphCanon lists graph-backed alternatives at [awesome-RLHF alternatives](/tools/opendilab-awesome-rlhf/alternatives) and [CodeRL alternatives](/tools/salesforce-coderl/alternatives) ([awesome-RLHF markdown twin](/tools/opendilab-awesome-rlhf/alternatives.md), [CodeRL markdown twin](/tools/salesforce-coderl/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-salesforce-coderl.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, awesome-RLHF or CodeRL? awesome-RLHF: Steady. CodeRL: 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 awesome-RLHF and CodeRL? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-RLHF trust report](/tools/opendilab-awesome-rlhf/trust); [CodeRL trust report](/tools/salesforce-coderl/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/_