Q-learning excels in learning from feedback within sequential decision-making tasks but often requires extensive sampling to achieve significant improvements. While reward shaping can enhance learning efficiency, non-potential-based methods introduce biases that affect performance, and potential-based reward shaping, though unbiased, lacks the ability to provide heuristics for state-action pairs, limiting its effectiveness in complex environments. Large language models (LLMs) can achieve zero-shot learning for simpler tasks, but they suffer from low inference speeds and occasional hallucinations. To address these challenges, we propose \textbf{LLM-guided Q-learning}, a framework that leverages LLMs as heuristics to aid in learning the Q-function for reinforcement learning. Our theoretical analysis demonstrates that this approach adapts to hallucinations, improves sample efficiency, and avoids biasing final performance. Experimental results show that our algorithm is general, robust, and capable of preventing ineffective exploration.
Even though most interfaces in the real world are discrete, no efficient way exists to train neural networks to make use of them, yet. We enhance an Interaction Network (a Reinforcement Learning architecture) with discrete interfaces and train it on the generalized Dyck language. This task requires an understanding of hierarchical structures to solve, and has long proven difficult for neural networks. We provide the first solution based on learning to use discrete data structures. We encountered unexpected anomalous behavior during training, and utilized pre-training based on execution traces …
ReEvo: Large Language Models as Hyper-Heuristics with Reflective Evolution
Peking University（北京大学）；Southeast University（东南大学）；Singapore Management University（新加坡管理大学）；KAIST（韩国科学技术院）；PKU-Wuhan Institute for Artificial Intelligence（北京大学武汉人工智能研究院）
Heuristic Classification of Thoughts Prompting (HCoT): Integrating Expert System Heuristics for Structured Reasoning into Large Language Models
Computer Network Information Center, Chinese Academy of Sciences（中国科学院计算机网络信息中心）；Shenyang Aerospace University（沈阳航空航天大学）；ZGC LAB, Beijing,China（北京ZGC实验室）；Institute for Network Sciences and Cyberspace, Tsinghua University（清华大学网络科学与网络空间研究院）
Discovering Heuristics with Large Language Models (LLMs) for Mixed-Integer Programs: Single-Machine Scheduling
Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA（弗吉尼亚理工大学工业与系统工程系）；Department of Computer Science, Virginia Tech, Arlington, VA（弗吉尼亚理工大学计算机科学系）
Prompting Video-Language Foundation Models with Domain-specific Fine-grained Heuristics for Video Question Answering
School of Information Science and Technology, Hangzhou Normal University（杭州师范大学信息科学与技术学院）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；School of Computer Science and Technology, University of Chinese Academy of Sciences（中国科学院大学计算机科学与技术学院）；School of Computer Science and Technology, Hangzhou Dianzi University（杭州电子科技大学计算机科学与技术学院）
Heuristic-Induced Multimodal Risk Distribution Jailbreak Attack for Multimodal Large Language Models
Sun Yat-Sen University（中山大学）；Zhejiang University（浙江大学）；BraneMatrix AI；Nanyang Technological University（南洋理工大学）；Alibaba Group（阿里巴巴集团）；Renmin University of China（中国人民大学）
REMoH: A Reflective Evolution of Multi-objective Heuristics approach via Large Language Models
Vicomtech Foundation, Basque Research and Technology Alliance (BRTA)（维克姆技基金会、巴斯克研究与技术联盟（BRTA））；Department of Computer Sciences and Artificial Intelligence, University of the Basque Country (UPV/EHU)（巴斯克国家大学（UPV/EHU）计算机科学与人工智能系）；School of Applied Sciences and Engineering, Universidad EAFIT（埃法伊特大学应用科学与工程学院）；HiTZ Basque Center for Language Technology, University of the Basque Country (UPV/EHU)（巴斯克国家大学（UPV/EHU）HiTZ巴斯克语言技术中心）
Discovering heuristics in a complex SAT solver with large language models
School of Data Science, Fudan University, Shanghai, China（复旦大学数据科学学院，上海，中国）；Key Laboratory of System Software, Institute of Software, Chinese Academy of Sciences, Beijing, China（中国科学院软件研究所系统软件重点实验室，北京，中国）；SeedMath Technology Limited, Beijing, China（SeedMath技术有限公司，北京，中国）
Guiding Large Language Models with Genetic Programming-Evolved Heuristic Knowledge for Dynamic Multi-Mode Project Scheduling
Centre for Data Science and Artificial Intelligence（数据科学与人工智能中心）；School of Engineering and Computer Science（工程与计算机科学学院）；Victoria University of Wellington（惠灵顿维多利亚大学）
Enhancing Nursing and Elderly Care with Large Language Models: An AI-Driven Framework
Academy for Engineering and Technology, Fudan University（复旦大学工程技术学院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Guilin University of Electronic Technology（桂林电子科技大学）；Shanghai Jiao Tong University（上海交通大学）
