Papers with Optimization
Revisiting OPRO: The Limitations of Small-Scale LLMs as Optimizers (2024.findings-acl)
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| Challenge: | Recent studies aim to enhance the efficacy of Large Language Models (LLMs) through strategic prompting. |
| Approach: | They propose to revisit the optimization by prompting approach for small-scale LLMs . they suggest future prompting engineering to consider both model capabilities and computational costs . |
| Outcome: | The proposed approach shows limited effectiveness in small-scale LLMs, with limited inference capabilities constraining optimization ability. |
Crossroads of Optimization under Uncertainty: How to Choose the Optimal Model (2026.findings-acl)
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| Challenge: | Existing approaches to Optimization under Uncertainty (OuU) have inherent limitations and advantages. |
| Approach: | They propose a framework that automates the modeling and solving of six types of uncertainty models and generates mapping pairs to explore the potential relationship between optimization problems and optimal models. |
| Outcome: | The proposed framework achieves superior performance even on specific model types, with correlation analysis showing that data scale and specific scenario significantly influence model selection. |
Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization (2024.acl-long)
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Wenqi Zhang, Ke Tang, Hai Wu, Mengna Wang, Yongliang Shen, Guiyang Hou, Zeqi Tan, Peng Li, Yueting Zhuang, Weiming Lu
| Challenge: | Large Language Models (LLMs) are designed as specific task solvers with sophisticated prompt engineering, but are inherently incapacitating to address complex dynamic scenarios. |
| Approach: | They propose an LLM-based agent with policy-level reflection and optimization that can learn from interactive experiences and progressively elevate its behavioral policy. |
| Outcome: | The proposed agent outperforms vanilla LLM and specialized models in blackjack and Texas hold’em. |
StraGo: Harnessing Strategic Guidance for Prompt Optimization (2024.findings-emnlp)
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Yurong Wu, Yan Gao, Bin Zhu, Zineng Zhou, Xiaodi Sun, Sheng Yang, Jian-Guang Lou, Zhiming Ding, Linjun Yang
| Challenge: | Existing methods for prompt optimization often lead to prompt drifting, wherein newly generated prompts canadversely impact previously successful cases while addressing failures. |
| Approach: | They propose a method to mitigate prompt drifting by integrating in-context learning to formulate specific, actionable strategies for prompt optimization. |
| Outcome: | The proposed approach mitigates prompt drifting by leveraging insights from both successful and failed cases to identify critical factors for achieving optimization objectives. |
Structured Preference Optimization for Vision-Language Long-Horizon Task Planning (2025.emnlp-main)
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Xiwen Liang, Min Lin, Weiqi Ruan, Rongtao Xu, Yuecheng Liu, Jiaqi Chen, Bingqian Lin, Yuzheng Zhuang, Xiaodan Liang
| Challenge: | Existing vision-language planning methods struggle with long-horizon reasoning in dynamic environments due to the difficulty of training models to generate high-quality reasoning processes. |
| Approach: | They propose a framework that enhances reasoning and action selection for long-horizon task planning through structured evaluation and optimized training. |
| Outcome: | The proposed framework outperforms existing methods on short-horizon tasks but struggles with long-horizon reasoning in dynamic environments. |
SPIO: Ensemble and Selective Strategies via LLM-Based Multi-Agent Planning in Automated Data Science (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) have enabled dynamic reasoning in automated data analytics, but rigid, single-path workflows restrict strategic exploration and often lead to suboptimal outcomes. |
| Approach: | a new framework replaces rigid workflows with adaptive, multi-path planning . the framework offers two operating modes: SPIO-S and SPIO -E . |
| Outcome: | a new framework outperforms state-of-the-art pipelines on Kaggle and OpenML benchmarks. |
DecoupledESC: Enhancing Emotional Support Generation via Strategy-Response Decoupled Preference Optimization (2025.findings-emnlp)
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| Challenge: | Existing ESC data entangles psychological strategies and response content, making it difficult to construct high-quality preference pairs. |
| Approach: | They propose a Decoupled ESC framework that decomposes the ESC task into two sequential subtasks: strategy planning and empathic response generation. |
| Outcome: | The proposed framework outperforms baselines, reducing preference bias and improving response quality. |
SAFO: Stable Adaptive Fairness Optimization for LLM-Based Social Survey Simulation (2026.acl-long)
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| Challenge: | Social survey simulations are increasingly used to improve minority performance and social-welfare metrics. |
| Approach: | They propose a dynamic utility–fairness optimization framework for LLM-based survey simulation that explicitly targets fairness and training stability. |
| Outcome: | The proposed framework improves minority performance and social-welfare metrics on three large-scale survey datasets from China, the U.S. and Europe. |