DLPO : Towards a Robust, Efficient, and Generalizable Prompt Optimization Framework from a Deep-Learning Perspective (2025)

Abstract: Large Language Models (LLMs) have achieved remarkable success across diverse tasks, largely driven by well-designed prompts. However, crafting and selecting such prompts often requires considerable human effort, significantly limiting its scalability. To mitigate this, recent studies have explored automated prompt optimization as a promising solution. Despite these efforts, existing methods still face critical challenges in robustness, efficiency, and generalization . To systematically address these challenges, we first conduct an empirical analysis to identify the limitations of current reflection-based prompt optimization paradigm. Building on these insights, we propose 7 innovative approaches inspired by traditional deep learning paradigms for prompt optimization (DLPO), seamlessly integrating these concepts into text-based gradient optimization. Through these advancements, we progressively tackle the aforementioned challenges and validate our methods through extensive experimentation. We hope our study not only provides valuable guidance for future research but also offers a comprehensive understanding of the challenges and potential solutions in prompt optimization.

## introduction large language models (llms) have demonstrated remarkable achievements across diverse applications @xcite @xcite @xcite . notably, the performance of llms highly relies on well-crafted prompts @xcite . given their sensitivity to prompts, selecting optimal prompts is crucial for maximizing their performance in downstream tasks, a process known as prompt engineering (reynolds * equal contribution † corresponding author + + + textual learning rate textual dropout accuracy lack of robustness + robust low efficiency efficient limited generalization textual regularization generalizable convergence step convergence step accuracy textual momentum textual contrastive learning textual learning rate decay accuracy train test accuracy optimization step optimization step textual simulate annealing + + + train test large performance gap! small gap d at as et 1 d at as et 2 d at as et 3 and @xcite , which requires substantial human effort @xcite . to address this, researchers have developed automated prompt optimization (po) techniques, such as reinforcement learning-based methods @xcite @xcite , search-based methods @xcite @xcite , all aiming to improve efficiency and stability. among these methods, a dominant paradigm leverages llms' reflection capabilities, optimizing prompts based on feedback from external environments @xcite . following @xcite and @xcite , this paradigm draws inspiration from neural network training, reinterpreting key concepts: treating prompts as model parameters, output metrics as loss, and llm reflection as gradients within textual expression. such paradigms has proven to be a powerful and widely adopted tool for various downstream tasks @xcite @xcite . despite significant advancements in automated po, our preliminary exploration reveals that existing methods encounter practical limitations that hinder their widespread adoption. these challenges can be categorized into three key issues, as illustrated in fig. 1: (1) lack of robustness. current update methods often exhibit substantial oscillations and instability during the update process. (2) low efficiency. existing po strategies require massive iterations to achieve ideal prompts. (3) limited generalizability. while existing methods perform well on in-domain tasks present in training data, they struggle with distributionally out-of-domain tasks, which extremely restricts their applicability in real-world scenarios. to address this, we first perform a systematic review of the prevailing reflection-based po paradigm to identify its limitations. inspired by traditional deep learning approaches, we propose novel techniques for dlpo to enhance its po capabilities. traditional machine learning (ml) paradigm considers prompts as a single module, and relies on monotonic and uncontrollable gradient optimization process. in contrast, we view the semantic dependencies within complex prompts as a neural network, enabling the application of advanced deep learning methods. (1) unlike traditional ml approaches, our method employs textual dropout, which randomly discards certain prompt sentences to improve robustness. additionally, we use textual learning rate and textual simulated annealing to better control the optimization direction, enhancing robustness over monotonic gradient optimization. @xcite in deep learning, optimization speed often depends on the gradient optimization difficulty. to accelerate convergence, we apply textual learning rate decay, textual momentum, and textual contrastive learning for more efficient gradient optimization. (3) unlike the single prompt modularization in the ml paradigm, our method integrates textual regularization to prevent overcomplication of prompt sentences, ensuring that the model remains concise. the correspondence between all seven methods we propose and classical deep learning methods is shown in to evaluate the effectiveness of our proposed approach, we conduct extensive experiments on five datasets covering multiple tasks. the results indicate that our method significantly enhances the stability, efficiency, and generalizability of prompt optimization. notably, it outperforms the previous (1) textual forward (2) textual backward gradient (𝒢 "#$" ) the primary reason why the original prompt works is that it is concise, making it easy to understand and providing clear guidance… the main rationale for its failure is that the prompt is too brief, which can make it challenging to address more specific or complex tasks… state-of-the-art method by 8.1% and even surpasses manually designed prompts, demonstrating its superiority in automated prompt optimization. the contributions of this work are summarized as follows: ## experiment setups we utilize several benchmarks to evaluate the effectiveness of our proposed methods, including gsm8k @xcite , math @xcite , biggsm (bgsm) @xcite , bigbenchhard object counting (bbh) @xcite , and mgsm @xcite . the detailed descriptions can be found in appendix c.1. ## preliminary analysis in this section, we explore the following three key drawbacks for current reflection based prompt optimization paradigm. ## exploration in this section, we solve the above three problems using methods inspired by traditional deep learning step by step. the detailed implementation of our methods can be found in appendix b. ## best practices by systematically integrating various optimization methods, we progressively improve update robustness, generalization, and efficiency. however, identifying the optimal combination of these techniques remains an open challenge. to address this, we conduct extensive experiments on biggsm, mgsm, and bbh, evaluating different method combinations to determine the most effective approach. our findings indicate that the optimal configuration includes tlrd, tsa, tcl, and tregu, collectively referred to as dlpo. to assess its effectiveness, we compared our approach against several baselines: (1) textgrad (tg) @xcite , a gradient-based prompt optimization method. (2) apo @xcite , a widely recognized automatic prompt optimization technique. (3) hu-man, a human-optimized prompt method derived from the biggsm dataset. as shown in ## related work early prompt optimization methods relied on internal access to llms @xcite @xcite @xcite . however, these methods often require access to the logits or internal states of llms, which is infeasible for those only accessible through apis @xcite . to overcome these limitations, recent research has shifted toward exploring gradient-free methods, such as reinforcement learning based methods @xcite @xcite , search-based methods @xcite and other techniques like evolutionary algorithms @xcite and boosting @xcite . among them, reflectionbased methods have gained significant attention due to their ability to iteratively refine prompts while maintaining interpretability. several notable frameworks exemplify this paradigm, such as pro-tegi(apo) @xcite , textgrad (yuksekgonul et al., 2024), promptagent @xcite , pe2 @xcite , and gpo @xcite . apart from the above methods, researchers have also explored key challenges in llm-based prompt optimization. @xcite revealed that existing prompt optimization methods are vulnerable to distribution shifts, a critical issue for real-world applications. @xcite conducted a comprehensive analysis of llm-based prompt optimization, introducing a novel method that directly optimizes the target model's behavior in a more controllable manner. these advances in automated prompt optimization align with our research direction. however, as noted in our problem statement, significant challenges remain in achieving robust, efficient, and generalizable prompt optimization, limiting broader application. we are the first to systematically address these issues, introducing innovative techniques inspired by traditional deep learning to overcome these challenges. ## conclusion through empirical analysis, we identify key limitations in current reflection-based prompt optimization methods, particularly in robustness, efficiency, and generalization. to address these issues, we introduce novel techniques inspired by traditional deep learning paradigms, integrating concepts like gradient modulation, regularization, and adaptive learning into text-based optimization. our approach enhances stability, convergence speed, and generalizability in a systematic manner. experiments on diverse tasks and datasets, including bbh, gsm8k, biggsm, math, and mgsm, validate the effectiveness of our methods. by addressing core challenges in prompt optimization, our work provides valuable insights and guides future research in this field.