Related papers: Prompt Optimization Via Diffusion Language Models

Prompt Optimization Via Diffusion Language Models

URL: http://arxiv.org/abs/2602.18449v1
Date: Fri, 30 Jan 2026 00:00:54 GMT
Title: Prompt Optimization Via Diffusion Language Models
Authors: Shiyu Wang, Haolin Chen, Liangwei Yang, Jielin Qiu, Rithesh Murthy, Ming Zhu, Zixiang Chen, Silvio Savarese, Caiming Xiong, Shelby Heinecke, Huan Wang,
Abstract summary: We propose a diffusion-based framework for prompt optimization.<n>Our method enables flexible, span-level prompt updates without requiring access or modifying the downstream language model.<n>We show that moderate diffusion step counts provide the best balance between refinement quality and stability.
Score: 73.9599434962714
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
Abstract: We propose a diffusion-based framework for prompt optimization that leverages Diffusion Language Models (DLMs) to iteratively refine system prompts through masked denoising. By conditioning on interaction traces, including user queries, model responses, and optional feedback, our method enables flexible, span-level prompt updates without requiring gradient access or modifying the downstream language model. Across diverse benchmarks (e.g., $τ$-bench, SST-2, SST-5), DLM-optimized prompts consistently improve the performance of a frozen target LLM (e.g., GPT-4o-mini). We further show that moderate diffusion step counts provide the best balance between refinement quality and stability. These results highlight diffusion-based prompt optimization as a general, model-agnostic, and scalable approach for enhancing LLM performance through iterative prompt refinement.

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