--- title: "Awesome-LLMs-ICLR-24 vs LLM4AlgorithmDesign" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/azminewasi-awesome-llms-iclr-24-vs-feiliu36-llm4algorithmdesign" tools: ["azminewasi-awesome-llms-iclr-24", "feiliu36-llm4algorithmdesign"] --- # Awesome-LLMs-ICLR-24 vs LLM4AlgorithmDesign *GraphCanon updated Aug 8, 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 LLM4AlgorithmDesign if lLM4AlgorithmDesign is a valuable resource for researchers and practitioners focusing on the intersection of large language models with algorithm design and optimization. [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. [LLM4AlgorithmDesign](https://github.com/FeiLiu36/LLM4AlgorithmDesign) has 385 stars, 40 forks, and 0 open issues, last pushed Mar 31, 2026. Figures are from public GitHub metadata via [Awesome-LLMs-ICLR-24's repository](https://github.com/azminewasi/Awesome-LLMs-ICLR-24) and [LLM4AlgorithmDesign's repository](https://github.com/FeiLiu36/LLM4AlgorithmDesign). | | [Awesome-LLMs-ICLR-24](/tools/azminewasi-awesome-llms-iclr-24.md) | [LLM4AlgorithmDesign](/tools/feiliu36-llm4algorithmdesign.md) | | --- | --- | --- | | Tagline | Compilation of LLM papers from ICLR 2024 | A Collection on Large Language Models for Optimization | | Stars | 72 | 385 | | Forks | 5 | 40 | | Open issues | 0 | 0 | | 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. | LLM4AlgorithmDesign is a valuable resource for researchers and practitioners focusing on the intersection of large language models with algorithm design and optimization. | | Persona | - | - | | Runtime | - | - | | License | MIT | - | | Categories | Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | Evaluation & Observability, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Awesome-LLMs-ICLR-24](/tools/azminewasi-awesome-llms-iclr-24.md) | [LLM4AlgorithmDesign](/tools/feiliu36-llm4algorithmdesign.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 856d | 128d | | Full report | [trust report](/tools/azminewasi-awesome-llms-iclr-24/trust.md) | [trust report](/tools/feiliu36-llm4algorithmdesign/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: LLM4AlgorithmDesign - **Pricing:** freemium - As the repository's license information and language are unknown, assume it to be free but use only for research purpose - **Requirements:** - The main requirement is an interest in large Language Models (LLMs) in algorithm design and optimization.; - Familiarity with Python may be an advantage, considering the mentioned LLM4AD platform is Python-based. - **Adopt for:** LLM4AlgorithmDesign is a valuable resource for researchers and practitioners focusing on the intersection of large language models with algorithm design and optimization. ## Choose when ### Choose Awesome-LLMs-ICLR-24 if… - Tags unique to Awesome-LLMs-ICLR-24: large-language-model, llm-agent, llm-evaluation, llm-framework. - Also covers Developer Tools, Inference & Serving, Model Training. - 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 LLM4AlgorithmDesign if… - Pricing: As the repository's license information and language are unknown, assume it to be free but use only for research purpose. - Requirements: - The main requirement is an interest in large Language Models (LLMs) in algorithm design and optimization.; - Familiarity with Python may be an advantage, considering the mentioned LLM4AD platform is Python-based.. - Tags unique to LLM4AlgorithmDesign: algorithm design, large language models, optimization-algorithms. - - You are a researcher who needs access to a comprehensive set of references and papers focused specifically on using large language models (LLMs) in algorithm design and optimization. ## 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 LLM4AlgorithmDesign - - If you require a hands-on development framework but without the specific focus on optimizing algorithms through large language models. - - You are looking for a platform with active development contributions from users. LLM4AlgorithmDesign primarily serves as a repository of references, which means its primary utility is in referencing - - This tool is not suitable for those seeking direct implementation guidance or code snippets for algorithm optimization without additional research. ## Common questions ### What is the difference between Awesome-LLMs-ICLR-24 and LLM4AlgorithmDesign? Awesome-LLMs-ICLR-24: Compilation of LLM papers from ICLR 2024. LLM4AlgorithmDesign: A Collection on Large Language Models for Optimization. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-LLMs-ICLR-24 over LLM4AlgorithmDesign? Choose Awesome-LLMs-ICLR-24 over LLM4AlgorithmDesign when Tags unique to Awesome-LLMs-ICLR-24: large-language-model, llm-agent, llm-evaluation, llm-framework; Also covers Developer Tools, Inference & Serving, Model Training; 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 LLM4AlgorithmDesign over Awesome-LLMs-ICLR-24? Choose LLM4AlgorithmDesign over Awesome-LLMs-ICLR-24 when Pricing: As the repository's license information and language are unknown, assume it to be free but use only for research purpose; Requirements: - The main requirement is an interest in large Language Models (LLMs) in algorithm design and optimization.; - Familiarity with Python may be an advantage, considering the mentioned LLM4AD platform is Python-based.; Tags unique to LLM4AlgorithmDesign: algorithm design, large language models, optimization-algorithms; - You are a researcher who needs access to a comprehensive set of references and papers focused specifically on using large language models (LLMs) in algorithm design and optimization. ### 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 LLM4AlgorithmDesign? - If you require a hands-on development framework but without the specific focus on optimizing algorithms through large language models. - You are looking for a platform with active development contributions from users. LLM4AlgorithmDesign primarily serves as a repository of references, which means its primary utility is in referencing - This tool is not suitable for those seeking direct implementation guidance or code snippets for algorithm optimization without additional research. ### Is Awesome-LLMs-ICLR-24 or LLM4AlgorithmDesign more popular on GitHub? LLM4AlgorithmDesign has more GitHub stars (385 vs 72). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-LLMs-ICLR-24 and LLM4AlgorithmDesign open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to Awesome-LLMs-ICLR-24 or LLM4AlgorithmDesign? GraphCanon lists graph-backed alternatives at [Awesome-LLMs-ICLR-24 alternatives](/tools/azminewasi-awesome-llms-iclr-24/alternatives) and [LLM4AlgorithmDesign alternatives](/tools/feiliu36-llm4algorithmdesign/alternatives) ([Awesome-LLMs-ICLR-24 markdown twin](/tools/azminewasi-awesome-llms-iclr-24/alternatives.md), [LLM4AlgorithmDesign markdown twin](/tools/feiliu36-llm4algorithmdesign/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-feiliu36-llm4algorithmdesign.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 LLM4AlgorithmDesign? Awesome-LLMs-ICLR-24: Dormant. LLM4AlgorithmDesign: Slowing. 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 LLM4AlgorithmDesign? 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); [LLM4AlgorithmDesign trust report](/tools/feiliu36-llm4algorithmdesign/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/_