--- title: "Instruction-Tuning-Papers vs LLM-Agent-Paper-List" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/sinclaircoder-instruction-tuning-papers-vs-woooodyy-llm-agent-paper-list" tools: ["sinclaircoder-instruction-tuning-papers", "woooodyy-llm-agent-paper-list"] --- # Instruction-Tuning-Papers vs LLM-Agent-Paper-List *GraphCanon updated Jul 12, 2026* ## Verdict Pick Instruction-Tuning-Papers if instruction-Tuning-Papers is a curated reading list focused on the instruction-tuning domain for language models; pick LLM-Agent-Paper-List if lLM-Agent-Paper-List is a meticulously curated collection focused on essential research papers related to AI agents built using Large Language Models, encompassing reinforcement learning frameworks and methodologies. It’. [Instruction-Tuning-Papers](https://github.com/SinclairCoder/Instruction-Tuning-Papers) reports 769 GitHub stars, 23 forks, and 0 open issues, last pushed Jul 20, 2023. [LLM-Agent-Paper-List](https://arxiv.org/abs/2309.07864) has 8.2k stars, 492 forks, and 25 open issues, last pushed Sep 12, 2025. Figures are from public GitHub metadata via [Instruction-Tuning-Papers's repository](https://github.com/SinclairCoder/Instruction-Tuning-Papers) and [LLM-Agent-Paper-List's repository](https://github.com/WooooDyy/LLM-Agent-Paper-List). | | [Instruction-Tuning-Papers](/tools/sinclaircoder-instruction-tuning-papers.md) | [LLM-Agent-Paper-List](/tools/woooodyy-llm-agent-paper-list.md) | | --- | --- | --- | | Tagline | Reading list of Instruction-tuning papers. | The paper list of the 86-page SCIS cover paper "The Rise and Potential of Large Language Model Based Agents: A Survey" by Zhiheng Xi et al. | | Stars | 769 | 8,159 | | Forks | 23 | 492 | | Open issues | 0 | 25 | | Language | - | - | | Adopt for | Instruction-Tuning-Papers is a curated reading list focused on the instruction-tuning domain for language models. | LLM-Agent-Paper-List is a meticulously curated collection focused on essential research papers related to AI agents built using Large Language Models, encompassing reinforcement learning frameworks and methodologies. It’ | | Persona | - | - | | Runtime | - | - | | License | - | - | | Categories | Model Training | Model Training, LLM Frameworks, AI Agents | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Instruction-Tuning-Papers](/tools/sinclaircoder-instruction-tuning-papers.md) | [LLM-Agent-Paper-List](/tools/woooodyy-llm-agent-paper-list.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 1087d | 302d | | Open issues (now) | 0 | 25 | | Full report | [trust report](/tools/sinclaircoder-instruction-tuning-papers/trust.md) | [trust report](/tools/woooodyy-llm-agent-paper-list/trust.md) | ## Decision facts: Instruction-Tuning-Papers - **Adopt for:** Instruction-Tuning-Papers is a curated reading list focused on the instruction-tuning domain for language models. ## Decision facts: LLM-Agent-Paper-List - **Adopt for:** LLM-Agent-Paper-List is a meticulously curated collection focused on essential research papers related to AI agents built using Large Language Models, encompassing reinforcement learning frameworks and methodologies. It’ ## Choose when ### Choose Instruction-Tuning-Papers if… - Tags unique to Instruction-Tuning-Papers: multi-task-learning, instruction-tuning, natural-language-processing, cross-task-generalization. - When you're looking to enhance your understanding of how natural language instructions can empower language models in diverse tasks. - Leaner open-issue backlog (0). ### Choose LLM-Agent-Paper-List if… - Tags unique to LLM-Agent-Paper-List: llm, nlp, survey, agent. - Also covers LLM Frameworks, AI Agents. - Ideal for researchers and developers deeply interested in the theory and applications of LLM-based agents. ## When NOT to use Instruction-Tuning-Papers - Avoid this resource if you are looking for tools or frameworks to implement instruction tuning rather than theoretical understanding. - Not suitable for users in need of a broader overview beyond specific academic papers on language model training methodologies. - If your interest lies more in general NLP resources or comprehensive toolkits, Instruction-Tuning-Papers may not cover all aspects. ## When NOT to use LLM-Agent-Paper-List - Not suitable if your focus is on traditional machine learning models without a language model foundation. - May not be as helpful if you are looking for resources outside the scope of large language model-based agents, such as purely statistical modeling techniques. ## Common questions ### What is the difference between Instruction-Tuning-Papers and LLM-Agent-Paper-List? Instruction-Tuning-Papers: Reading list of Instruction-tuning papers.. LLM-Agent-Paper-List: The paper list of the 86-page SCIS cover paper "The Rise and Potential of Large Language Model Based Agents: A Survey" by Zhiheng Xi et al.. See the comparison table for live GitHub stats and shared categories. ### When should I choose Instruction-Tuning-Papers over LLM-Agent-Paper-List? Choose Instruction-Tuning-Papers over LLM-Agent-Paper-List when Tags unique to Instruction-Tuning-Papers: multi-task-learning, instruction-tuning, natural-language-processing, cross-task-generalization; When you're looking to enhance your understanding of how natural language instructions can empower language models in diverse tasks; Leaner open-issue backlog (0). ### When should I choose LLM-Agent-Paper-List over Instruction-Tuning-Papers? Choose LLM-Agent-Paper-List over Instruction-Tuning-Papers when Tags unique to LLM-Agent-Paper-List: llm, nlp, survey, agent; Also covers LLM Frameworks, AI Agents; Ideal for researchers and developers deeply interested in the theory and applications of LLM-based agents. ### When should I avoid Instruction-Tuning-Papers? Avoid this resource if you are looking for tools or frameworks to implement instruction tuning rather than theoretical understanding. Not suitable for users in need of a broader overview beyond specific academic papers on language model training methodologies. If your interest lies more in general NLP resources or comprehensive toolkits, Instruction-Tuning-Papers may not cover all aspects. ### When should I avoid LLM-Agent-Paper-List? Not suitable if your focus is on traditional machine learning models without a language model foundation. May not be as helpful if you are looking for resources outside the scope of large language model-based agents, such as purely statistical modeling techniques. ### Is Instruction-Tuning-Papers or LLM-Agent-Paper-List more popular on GitHub? LLM-Agent-Paper-List has more GitHub stars (8,159 vs 769). Stars measure visibility, not whether either tool fits your constraints. ### Are Instruction-Tuning-Papers and LLM-Agent-Paper-List open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to Instruction-Tuning-Papers or LLM-Agent-Paper-List? GraphCanon lists graph-backed alternatives at [Instruction-Tuning-Papers alternatives](/tools/sinclaircoder-instruction-tuning-papers/alternatives) and [LLM-Agent-Paper-List alternatives](/tools/woooodyy-llm-agent-paper-list/alternatives) ([Instruction-Tuning-Papers markdown twin](/tools/sinclaircoder-instruction-tuning-papers/alternatives.md), [LLM-Agent-Paper-List markdown twin](/tools/woooodyy-llm-agent-paper-list/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/sinclaircoder-instruction-tuning-papers-vs-woooodyy-llm-agent-paper-list.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Instruction-Tuning-Papers or LLM-Agent-Paper-List? Instruction-Tuning-Papers: Dormant. LLM-Agent-Paper-List: 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 Instruction-Tuning-Papers and LLM-Agent-Paper-List? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Instruction-Tuning-Papers trust report](/tools/sinclaircoder-instruction-tuning-papers/trust); [LLM-Agent-Paper-List trust report](/tools/woooodyy-llm-agent-paper-list/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=sinclaircoder-instruction-tuning-papers`](/api/graphcanon/graph?tool=sinclaircoder-instruction-tuning-papers) - 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/_