@inproceedings{azim-etal-2025-autodspy,
title = "{A}uto{DSP}y: Automating Modular Prompt Design with Reinforcement Learning for Small and Large Language Models",
author = "Azim, Nafew and
Alam, Abrar Ur and
Omar, Hasan Bin and
Jami, Abdullah Mohammad Muntasir Adnan and
Ahad, Jawad Ibn and
Kabir, Muhammad Rafsan and
Hossain, Md. Ismail and
Rahman, Fuad and
Amin, Mohammad Ruhul and
Rahman, Shafin and
Mohammed, Nabeel",
editor = "Potdar, Saloni and
Rojas-Barahona, Lina and
Montella, Sebastien",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = nov,
year = "2025",
address = "Suzhou (China)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-industry.192/",
pages = "2881--2896",
ISBN = "979-8-89176-333-3",
abstract = "Large Language Models (LLMs) excel at complexreasoning tasks, yet their performance hinges on the quality of their prompts and pipeline structures. Manual promptdesign, as used in frameworks like DSPy, poses significantlimitations: it is time-intensive, demands substantial expertise,and lacks scalability, restricting the widespread use of LLMsacross diverse applications. To overcome these challenges, weintroduce AutoDSPy, the first framework to fully automateDSPy pipeline construction using reinforcement learning (RL).AutoDSPy leverages an RL-tuned policy network to dynamicallyselect optimal reasoning modules{---}such as Chain-of-Thought forlogical tasks or ReAct for tool integration{---}along with inputoutput signatures and execution strategies, entirely eliminatingthe need for manual configuration. Experimental results on theGSM8K and HotPotQA benchmarks demonstrate that AutoDSPyoutperforms traditional DSPy baselines, achieving accuracy gainsof up to 4.3{\%} while reducing inference time, even with smallermodels like GPT-2 (127M). By integrating RL-based automation,AutoDSPy enhances both efficiency and accessibility, simplifyingthe development of structured, high-performing LLM solutionsand enabling scalability across a wide range of tasks"
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<abstract>Large Language Models (LLMs) excel at complexreasoning tasks, yet their performance hinges on the quality of their prompts and pipeline structures. Manual promptdesign, as used in frameworks like DSPy, poses significantlimitations: it is time-intensive, demands substantial expertise,and lacks scalability, restricting the widespread use of LLMsacross diverse applications. To overcome these challenges, weintroduce AutoDSPy, the first framework to fully automateDSPy pipeline construction using reinforcement learning (RL).AutoDSPy leverages an RL-tuned policy network to dynamicallyselect optimal reasoning modules—such as Chain-of-Thought forlogical tasks or ReAct for tool integration—along with inputoutput signatures and execution strategies, entirely eliminatingthe need for manual configuration. Experimental results on theGSM8K and HotPotQA benchmarks demonstrate that AutoDSPyoutperforms traditional DSPy baselines, achieving accuracy gainsof up to 4.3% while reducing inference time, even with smallermodels like GPT-2 (127M). By integrating RL-based automation,AutoDSPy enhances both efficiency and accessibility, simplifyingthe development of structured, high-performing LLM solutionsand enabling scalability across a wide range of tasks</abstract>
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%0 Conference Proceedings
%T AutoDSPy: Automating Modular Prompt Design with Reinforcement Learning for Small and Large Language Models
%A Azim, Nafew
%A Alam, Abrar Ur
%A Omar, Hasan Bin
%A Jami, Abdullah Mohammad Muntasir Adnan
%A Ahad, Jawad Ibn
%A Kabir, Muhammad Rafsan
%A Hossain, Md. Ismail
%A Rahman, Fuad
%A Amin, Mohammad Ruhul
%A Rahman, Shafin
%A Mohammed, Nabeel
%Y Potdar, Saloni
%Y Rojas-Barahona, Lina
%Y Montella, Sebastien
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou (China)
%@ 979-8-89176-333-3
%F azim-etal-2025-autodspy
%X Large Language Models (LLMs) excel at complexreasoning tasks, yet their performance hinges on the quality of their prompts and pipeline structures. Manual promptdesign, as used in frameworks like DSPy, poses significantlimitations: it is time-intensive, demands substantial expertise,and lacks scalability, restricting the widespread use of LLMsacross diverse applications. To overcome these challenges, weintroduce AutoDSPy, the first framework to fully automateDSPy pipeline construction using reinforcement learning (RL).AutoDSPy leverages an RL-tuned policy network to dynamicallyselect optimal reasoning modules—such as Chain-of-Thought forlogical tasks or ReAct for tool integration—along with inputoutput signatures and execution strategies, entirely eliminatingthe need for manual configuration. Experimental results on theGSM8K and HotPotQA benchmarks demonstrate that AutoDSPyoutperforms traditional DSPy baselines, achieving accuracy gainsof up to 4.3% while reducing inference time, even with smallermodels like GPT-2 (127M). By integrating RL-based automation,AutoDSPy enhances both efficiency and accessibility, simplifyingthe development of structured, high-performing LLM solutionsand enabling scalability across a wide range of tasks
%U https://aclanthology.org/2025.emnlp-industry.192/
%P 2881-2896
Markdown (Informal)
[AutoDSPy: Automating Modular Prompt Design with Reinforcement Learning for Small and Large Language Models](https://aclanthology.org/2025.emnlp-industry.192/) (Azim et al., EMNLP 2025)
ACL
- Nafew Azim, Abrar Ur Alam, Hasan Bin Omar, Abdullah Mohammad Muntasir Adnan Jami, Jawad Ibn Ahad, Muhammad Rafsan Kabir, Md. Ismail Hossain, Fuad Rahman, Mohammad Ruhul Amin, Shafin Rahman, and Nabeel Mohammed. 2025. AutoDSPy: Automating Modular Prompt Design with Reinforcement Learning for Small and Large Language Models. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 2881–2896, Suzhou (China). Association for Computational Linguistics.