--- title: "awesome-RLHF vs Awesome-LLM-in-Social-Science" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/opendilab-awesome-rlhf-vs-valuebyte-ai-awesome-llm-in-social-science" tools: ["opendilab-awesome-rlhf", "valuebyte-ai-awesome-llm-in-social-science"] --- # awesome-RLHF vs Awesome-LLM-in-Social-Science *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 Awesome-LLM-in-Social-Science if curate research papers on LLM applications in social science, covering topics like alignment, economics, policy, psychology, and more. [awesome-RLHF](https://github.com/opendilab/awesome-RLHF) reports 4.4k GitHub stars, 258 forks, and 6 open issues, last pushed May 20, 2026. [Awesome-LLM-in-Social-Science](https://github.com/ValueByte-AI/Awesome-LLM-in-Social-Science) has 639 stars, 48 forks, and 0 open issues, last pushed Jun 8, 2026. Figures are from public GitHub metadata via [awesome-RLHF's repository](https://github.com/opendilab/awesome-RLHF) and [Awesome-LLM-in-Social-Science's repository](https://github.com/ValueByte-AI/Awesome-LLM-in-Social-Science). | | [awesome-RLHF](/tools/opendilab-awesome-rlhf.md) | [Awesome-LLM-in-Social-Science](/tools/valuebyte-ai-awesome-llm-in-social-science.md) | | --- | --- | --- | | Tagline | A curated list of reinforcement learning with human feedback resources (continually updated) | Awesome papers involving LLMs in Social Science | | Stars | 4,422 | 639 | | Forks | 258 | 48 | | Open issues | 6 | 0 | | Language | - | - | | 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. | Curate research papers on LLM applications in social science, covering topics like alignment, economics, policy, psychology, and more. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | Evaluation & Observability, Model Training | Evaluation & Observability, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-RLHF](/tools/opendilab-awesome-rlhf.md) | [Awesome-LLM-in-Social-Science](/tools/valuebyte-ai-awesome-llm-in-social-science.md) | | --- | --- | --- | | Days since push | 89d | 49d | | Open issues (now) | 6 | 0 | | Stars delta | +9 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Full report | [trust report](/tools/opendilab-awesome-rlhf/trust.md) | [trust report](/tools/valuebyte-ai-awesome-llm-in-social-science/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: Awesome-LLM-in-Social-Science - **Adopt for:** Curate research papers on LLM applications in social science, covering topics like alignment, economics, policy, psychology, and more. ## Choose when ### Choose awesome-RLHF if… - License: awesome-RLHF is Apache-2.0, Awesome-LLM-in-Social-Science is MIT. - Tags unique to awesome-RLHF: deep-learning, depth-reinforcement-learning, human-feedback, reinforcement-learning. - When you are specifically interested in the resources that pertain to enhancing reinforcement learning algorithms with human feedback for developing advanced AI systems. ### Choose Awesome-LLM-in-Social-Science if… - License: Awesome-LLM-in-Social-Science is MIT, awesome-RLHF is Apache-2.0. - Tags unique to Awesome-LLM-in-Social-Science: alignment, economics, llm-agent, llm-evaluation. - Need to explore academic insights into LLM impacts on specific social areas ## 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 Awesome-LLM-in-Social-Science - Looking for a hands-on coding or practical implementation guide of LLMs - In need of real-time data analysis tools for immediate social science research outcomes ## Common questions ### What is the difference between awesome-RLHF and Awesome-LLM-in-Social-Science? awesome-RLHF: A curated list of reinforcement learning with human feedback resources (continually updated). Awesome-LLM-in-Social-Science: Awesome papers involving LLMs in Social Science. See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome-RLHF over Awesome-LLM-in-Social-Science? Choose awesome-RLHF over Awesome-LLM-in-Social-Science when License: awesome-RLHF is Apache-2.0, Awesome-LLM-in-Social-Science is MIT; Tags unique to awesome-RLHF: deep-learning, depth-reinforcement-learning, human-feedback, reinforcement-learning; 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 Awesome-LLM-in-Social-Science over awesome-RLHF? Choose Awesome-LLM-in-Social-Science over awesome-RLHF when License: Awesome-LLM-in-Social-Science is MIT, awesome-RLHF is Apache-2.0; Tags unique to Awesome-LLM-in-Social-Science: alignment, economics, llm-agent, llm-evaluation; Need to explore academic insights into LLM impacts on specific social areas. ### 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 Awesome-LLM-in-Social-Science? Looking for a hands-on coding or practical implementation guide of LLMs In need of real-time data analysis tools for immediate social science research outcomes ### Is awesome-RLHF or Awesome-LLM-in-Social-Science more popular on GitHub? awesome-RLHF has more GitHub stars (4,422 vs 639). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-RLHF and Awesome-LLM-in-Social-Science open source? Yes - both are open-source projects on GitHub (awesome-RLHF: Apache-2.0, Awesome-LLM-in-Social-Science: MIT). ### Where can I find alternatives to awesome-RLHF or Awesome-LLM-in-Social-Science? GraphCanon lists graph-backed alternatives at [awesome-RLHF alternatives](/tools/opendilab-awesome-rlhf/alternatives) and [Awesome-LLM-in-Social-Science alternatives](/tools/valuebyte-ai-awesome-llm-in-social-science/alternatives) ([awesome-RLHF markdown twin](/tools/opendilab-awesome-rlhf/alternatives.md), [Awesome-LLM-in-Social-Science markdown twin](/tools/valuebyte-ai-awesome-llm-in-social-science/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-valuebyte-ai-awesome-llm-in-social-science.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, awesome-RLHF or Awesome-LLM-in-Social-Science? awesome-RLHF: Steady. Awesome-LLM-in-Social-Science: 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 Awesome-LLM-in-Social-Science? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-RLHF trust report](/tools/opendilab-awesome-rlhf/trust); [Awesome-LLM-in-Social-Science trust report](/tools/valuebyte-ai-awesome-llm-in-social-science/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/_