--- title: "Awesome-LLMs-ICLR-24 vs awesome-RLHF" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/azminewasi-awesome-llms-iclr-24-vs-opendilab-awesome-rlhf" tools: ["azminewasi-awesome-llms-iclr-24", "opendilab-awesome-rlhf"] --- # Awesome-LLMs-ICLR-24 vs awesome-RLHF *GraphCanon updated Aug 17, 2026* ## Verdict Pick Awesome-LLMs-ICLR-24 if awesome-LLMs-ICLR-24 is an essential resource hub for researchers and developers working with large language models, focusing on LLM research papers accepted at ICLR in 2024; 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. [Awesome-LLMs-ICLR-24](https://github.com/azminewasi/Awesome-LLMs-ICLR-24) reports 72 GitHub stars, 5 forks, and 0 open issues, last pushed Apr 4, 2024. [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 [Awesome-LLMs-ICLR-24's repository](https://github.com/azminewasi/Awesome-LLMs-ICLR-24) and [awesome-RLHF's repository](https://github.com/opendilab/awesome-RLHF). | | [Awesome-LLMs-ICLR-24](/tools/azminewasi-awesome-llms-iclr-24.md) | [awesome-RLHF](/tools/opendilab-awesome-rlhf.md) | | --- | --- | --- | | Tagline | Compilation of LLM papers from ICLR 2024 | A curated list of reinforcement learning with human feedback resources (continually updated) | | Stars | 72 | 4,422 | | Forks | 5 | 258 | | Open issues | 0 | 6 | | Language | - | - | | Adopt for | Awesome-LLMs-ICLR-24 is an essential resource hub for researchers and developers working with large language models, focusing on LLM research papers accepted at ICLR in 2024. | 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 | Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | Evaluation & Observability, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Awesome-LLMs-ICLR-24](/tools/azminewasi-awesome-llms-iclr-24.md) | [awesome-RLHF](/tools/opendilab-awesome-rlhf.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Steady (60%) | | Days since push | 856d | 89d | | Open issues (now) | 0 | 6 | | Stars delta | Unknown | +9 (30d) | | Open issues delta | Unknown | 0 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/azminewasi-awesome-llms-iclr-24/trust.md) | [trust report](/tools/opendilab-awesome-rlhf/trust.md) | ## Decision facts: Awesome-LLMs-ICLR-24 - **Adopt for:** Awesome-LLMs-ICLR-24 is an essential resource hub for researchers and developers working with large language models, focusing on LLM research papers accepted at ICLR in 2024. ## 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 Awesome-LLMs-ICLR-24 if… - License: Awesome-LLMs-ICLR-24 is MIT, awesome-RLHF is Apache-2.0. - Tags unique to Awesome-LLMs-ICLR-24: large-language-model, llm-agent, llm-evaluation, llm-framework. - Also covers Developer Tools, Inference & Serving, LLM Frameworks. - If you are focusing specifically on recent advancements in Large Language Models discussed in the context of ICLR 2024, this repository will provide cutting-edge research papers and insights. ### Choose awesome-RLHF if… - License: awesome-RLHF is Apache-2.0, Awesome-LLMs-ICLR-24 is MIT. - Tags unique to awesome-RLHF: deep-learning, depth-reinforcement-learning, human-feedback, large language models. - 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 Awesome-LLMs-ICLR-24 - If you are looking for more general resources that cover a wider time span or different conferences than ICLR 2024. - For projects where immediate practical application of models without understanding the underlying research is prioritized over detailed exploration and analysis. ## 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 Awesome-LLMs-ICLR-24 and awesome-RLHF? Awesome-LLMs-ICLR-24: Compilation of LLM papers from ICLR 2024. 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 Awesome-LLMs-ICLR-24 over awesome-RLHF? Choose Awesome-LLMs-ICLR-24 over awesome-RLHF when License: Awesome-LLMs-ICLR-24 is MIT, awesome-RLHF is Apache-2.0; Tags unique to Awesome-LLMs-ICLR-24: large-language-model, llm-agent, llm-evaluation, llm-framework; Also covers Developer Tools, Inference & Serving, LLM Frameworks; If you are focusing specifically on recent advancements in Large Language Models discussed in the context of ICLR 2024, this repository will provide cutting-edge research papers and insights. ### When should I choose awesome-RLHF over Awesome-LLMs-ICLR-24? Choose awesome-RLHF over Awesome-LLMs-ICLR-24 when License: awesome-RLHF is Apache-2.0, Awesome-LLMs-ICLR-24 is MIT; Tags unique to awesome-RLHF: deep-learning, depth-reinforcement-learning, human-feedback, large language models; 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 Awesome-LLMs-ICLR-24? If you are looking for more general resources that cover a wider time span or different conferences than ICLR 2024. For projects where immediate practical application of models without understanding the underlying research is prioritized over detailed exploration and analysis. ### 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 Awesome-LLMs-ICLR-24 or awesome-RLHF more popular on GitHub? awesome-RLHF has more GitHub stars (4,422 vs 72). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-LLMs-ICLR-24 and awesome-RLHF open source? Yes - both are open-source projects on GitHub (Awesome-LLMs-ICLR-24: MIT, awesome-RLHF: Apache-2.0). ### Where can I find alternatives to Awesome-LLMs-ICLR-24 or awesome-RLHF? GraphCanon lists graph-backed alternatives at [Awesome-LLMs-ICLR-24 alternatives](/tools/azminewasi-awesome-llms-iclr-24/alternatives) and [awesome-RLHF alternatives](/tools/opendilab-awesome-rlhf/alternatives) ([Awesome-LLMs-ICLR-24 markdown twin](/tools/azminewasi-awesome-llms-iclr-24/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/azminewasi-awesome-llms-iclr-24-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, Awesome-LLMs-ICLR-24 or awesome-RLHF? Awesome-LLMs-ICLR-24: 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 Awesome-LLMs-ICLR-24 and awesome-RLHF? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-LLMs-ICLR-24 trust report](/tools/azminewasi-awesome-llms-iclr-24/trust); [awesome-RLHF trust report](/tools/opendilab-awesome-rlhf/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=azminewasi-awesome-llms-iclr-24`](/api/graphcanon/graph?tool=azminewasi-awesome-llms-iclr-24) - 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/_