by YoungDubbyDu · AI Tool · ★ 238
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LLM-Agent-Optimization 📢 News 2026 🎉 Our survey "A Survey on the Optimization of Large Language Model-based Agents" has been accepted by ACM Computing Surveys (CSUR)! The paper is now open access. We sincerely welcome your citations and feedback! This is the reading list for the survey "A Survey on the Optimization of Large Language Model-based Agents" (ACM CSUR), which systematically explores various optimization techniques for enhancing LLM-based agents. The survey categorizes existing works into parameter-driven optimization, parameter-free optimization, datasets and benchmarks, and real-world applications. We will keep adding papers and improving the list. Any suggestions and PRs are welcome! Parameter-driven Optimization Conventional Fine-Tuning-based FireAct : TOWARD LANGUAGE AGENT FINE-TUNING (arXiv 2023) [paper] [code] AgentTuning: Enabling Generalized Agent Abilities for LLMs (ACL-findings 2024) [paper] [code] SMART: Synergistic Multi-Agent Framework with Trajectory Learning for Knowledge-Intensive Tasks (arXiv 2024) [paper] [[code](https://github.com/yuesheng
| Stars | 238 |
| Forks | 22 |
| Category | AI Tool |
| Quality Score | 46.6387549341442/100 |
| Last Updated | 2026-06-17 |
| Created | 2025-03-07 |
| Est. Tokens | ~27k |
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Awesome-LLM-Agent-Optimization-Papers is This is the reading list for the survey "A Survey on the Optimization of LLM-based Agents ". We will keep adding papers and improving the list. Any suggestions and PRs are welcome!. It is categorized as a AI Tool with 238 GitHub stars.
You can find installation instructions and usage details in the Awesome-LLM-Agent-Optimization-Papers GitHub repository at github.com/YoungDubbyDu/Awesome-LLM-Agent-Optimization-Papers. The project has 238 stars and 22 forks, indicating an active community.
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