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Awesome-LLM-Prompt-Optimization: a curated list of advanced prompt optimization and tuning methods in Large Language Models
| Date | Stars |
|---|---|
| 2026-07-31 | 412 |
| 2026-08-06 | 412 |
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# Awesome-LLM-Prompt-Optimization \ [](https://github.com/hee9joon/Awesome-Diffusion-Models) [](https://opensource.org/licenses/MIT) [](https://github.com/chetanraj/awesome-github-badges) This repo aims to record advanced papers of LLM prompt tuning and automatic optimization (after 2022). We strongly encourage the researchers that want to promote their fantastic work to the LLM prompt optimization to make pull request to update their paper's information! --- ## Contents - [Papers](#papers) - [LLM Optimization](#llm-optimization) - [Fine-tune Methods](#fine-tune-methods) - [Programming](#programming) - [Human Perference and Feedback](#human-perference-and-feedback) - [Ensemble Methods](#ensemble-methods) - [Reinforcement Learning](#reinforcement-learning) - [Gradient-free Methods](#gradient-free-methods) - [In-Context Learning](#in-context-learning) - [Bayesian Optimization](#bayesian-optimization) --- ## LLM Optimization - [FutureAGI agent-opt](https://github.com/future-agi/agent-opt) [Auferet](https://auferet.com) - AI game master with persistent memory for your characters and uploaded lore; solo or multiplayer, with 5e and Pathfinder 2e modes. - [FutureAGI future-agi](https://github.com/future-agi/future-agi) - Open-source self-hostable end-to-end agent engineering and optimization platform unifying tracing, evals, simulations, datasets, gateway, and guardrails for LLM and AI agent applications. - [Weco — eval-driven optimization with LangSmith/Langfuse](https://weco.ai/blog/weco-langsmith-integration) - Runs autoresearch code/prompt optimization directly against your existing LangSmith and Langfuse datasets and evaluators. **Black-Box Prompt Optimization: Aligning Large Language Models without Model Training** \ *Jiale Cheng, Xiao Liu, Kehan Zheng, Pei Ke, Hongning Wang, Yuxiao Dong, Jie Tang, Minlie Huang* \ arXiv 2023. [[Paper](https://arxiv.org/abs/2311.04155)] [[Github](https://github.com/thu-coai/bpo)] \ 7 Nov 2023 **Language Model Decoding as Direct Metrics Optimization** \ *Haozhe Ji, Pei Ke, Hongning Wang, Minlie Huang* \ arXiv 2023. [[Paper](https://arxiv.org/abs/2310.01041)] \ 2 Oct 2023 **Large Language Models as Evolutionary Optimizers** \ *Shengcai Liu, Caishun Chen, Xinghua Qu, Ke Tang, Yew-Soon Ong* \ arXiv 2023 [[Paper](https://arxiv.org/abs/2310.19046)] \ 29 Oct 2023 **OptiMUS: Optimization Modeling Using MIP Solvers and large language models** \ *Ali AhmadiTeshnizi, Wenzhi Gao, Madeleine Udell* \ arXiv 2023. [[Paper](https://arxiv.org/abs/2310.06116)] [[Github](https://arxiv.org/abs/2310.06116)] \ 9 Oct 2023 **PromptAgent: Strategic Planning with Language Models Enables Expert-level Prompt Optimization** \ *Xinyuan Wang, Chenxi Li, Zhen Wang, Fan Bai, Haotian Luo, Jiayou Zhang, Nebojsa Jojic, Eric P. Xing, Zhiting Hu* \ arXiv 2023. [[Paper](https://arxiv.org/abs/2310.16427)] \ 25 Oct 2023 **Self-Taught Optimizer (STOP): Recursively Self-Improving Code Generation** \ *Eric Zelikman, Eliana Lorch, Lester Mackey, Adam Tauman Kalai* \ arXiv 2023. [[Paper](https://arxiv.org/abs/2310.02304)] \ 3 Oct 2023 **Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution** \ *Chrisantha Fernando, Dylan Banarse, Henryk Michalewski, Simon Osindero, Tim Rocktäschel* \ arXiv 2023. [[Paper](https://arxiv.org/abs/2309.16797)] \ 28 Sep 2023 **Connecting large language models with evolutionary algorithms yields powerful prompt optimizers** \ *Qingyan Guo, Rui Wang, Junliang Guo, Bei Li, Kaitao Song, Xu Tan, Guoqing Liu, Jiang Bian, Yujiu Yang* \ arXiv 2023. [[Paper](https://arxiv.org/abs/2309.08532)] \ 15 Sep 2023 **Large Language Models as Optimizers (OPRO)** \ *Chengrun Yang, Xuezhi Wang, Yifeng Lu, Hanxiao Liu, Q
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matched fp:69f1216681a0c7c3, topic:prompt, name:prompt optimization, desc:prompt optimization
matched fp:69f1216681a0c7c3, topic:large-language-models