Exploration-Exploitation Tradeoff

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3 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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Human-inspired, Task-Dimension-Guided Exploration for Efficient Learning in High Dimensions
Sep 29, 2026Fanyu Zhu, Jiahui An, Ni JiExploration-Exploitation Tradeoff RL Exploration
Beijing Institute for Brain Research, Chinese Academy of Medical Sciences & Peking Union Medical College, Chinese Institute for Brain Research, Beijing, Beijing Key Laboratory of Brain Science and Brain-Machine Interface · State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Signatures of semantic search in the activations of large language models
Sep 28, 2026Luke Leckie, Peter M. Todd, Jacob G. FosterExploration-Exploitation Tradeoff LLM Interpretability
Department of Informatics, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN · Cognitive Science Program, Indiana University, Bloomington, IN · Center for Possible Minds, Indiana University, Bloomington, IN +2
VRL-Bench: Benchmarking agents on computer control tasks under finite trial budgets
Sep 14, 2026Yu Bai, Yukai Miao, Dawei Wang +8RL Benchmarks Reinforcement Learning
Zhongguancun Laboratory · Tsinghua University · Network Management Center, China Mobile
Situation Aware Frontier Prioritization for Quadruped Search and Rescue
Aug 3, 2026Kevin Farias, Santiago Martin, Barbara Flores +5Exploration-Exploitation Tradeoff Robotics
Robotics and AI Lab, Technological University of Uruguay, Rivera, Uruguay
Rethinking Self-Evolution: A Constrained Exploration-Exploitation Process for Mitigating Skill Overfitting
Jul 29, 2026Hongqiang Lin, Chao Liu, Xiaofan Bai +4Exploration-Exploitation Tradeoff LLM Agent Skill Learning
Zhejiang University · Alibaba Group
Experimentation and Commitment under Reward Shifts
Jul 26, 2026Puping Jiang, Wei Tang, Renyu +1Multi-Armed Bandits Exploration-Exploitation Tradeoff
Philip
Collaborative Spatial Learning with Multi-LLM Agents in Networked Social Experiments
Jul 16, 2026Hao He, Chris J. Kuhlman, Xinwei DengExploration-Exploitation Tradeoff Multi-Agent LLMs
Department of Statistics, Virginia Tech, Blacksburg, VA, USA · Advanced Research Computing, Virginia Tech, Blacksburg, VA, USA
Learning from Local Walks on Dynamic Graphs with Bandit Feedback
Jul 12, 2026Sourav Chakraborty, Amit Kiran Rege, Claire Monteleoni +1Multi-Armed Bandits Graph Bandits
Department of Computer Science, University of Colorado, Boulder, CO 80309, USA · INRIA, Paris, France
Towards Self-Evolving Agents: A Human-Inspired Adaptive Exploration-Exploitation Framework for Genetic Network Programming
Jul 4, 2026Ali Kohan, Mohamad Roshanzamir, Roohallah Alizadehsani +1Exploration-Exploitation Tradeoff Genetic Programming
Department of Computer Engineering, Faculty of Engineering, Fasa University, Fasa 74617-81189, Iran · Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, Australia · Center of Excellence in Precision Medicine and Digital Health, Department of Physiology, Faculty of Dentistry, Chulalongkorn University, Bangkok 10330, Thailand. +2
Analysis of Parameter Settings for the Bat Algorithm Using Variance Evolution
Jun 26, 2026Xin-She Yang, Mehmet KaramanogluExploration-Exploitation Tradeoff Evolutionary Optimization
Faculty of Science and Technology, Middlesex University, The Burroughs, London NW4 4BT, United Kingdom.
Reward-free Pretraining for Reinforcement Learning via Occupancy Coverage Maximization
Jun 19, 2026Marco Pratticò, Pietro Novelli, Massimiliano Pontil +1Reinforcement Learning Exploration-Exploitation Tradeoff
Computational Statistics and Machine Learning - Istituto Italiano di Tecnologia, 16100 Genova, Italy · 2AI Centre, Computer Science Department, University College London, London, UK.
TreeSeeker: Tree-Structured Trial, Error, and Return in Deep Search
Jun 10, 2026Zhuofan Shi, Mingzhe Ma, Lu Wang +8Inference-Time Search Exploration-Exploitation Tradeoff
1Microsoft · 2East China Normal University
Exploratory Responsiveness and Adaptive Rigidity under AI-Assisted Optimization
Jun 8, 2026Balaraju BattuExploration-Exploitation Tradeoff Adaptive Learning
European University Institute, Florence, Italy · New York University Abu Dhabi, UAE
Algorithm for Contextual Queueing Bandits with Rate-Optimal Queue Length Regret
Jun 8, 2026Seoungbin Bae, Dabeen LeeExploration-Exploitation Tradeoff Regret Minimization
Department of Industrial & Systems Engineering, KAIST · Department of Mathematical Sciences, Seoul National University
Oblivious Learning and Collusive Pricing
Jun 3, 2026Yuhang Wu, Assaf ZeeviExploration-Exploitation Tradeoff Game Theory
Columbia Business School
Annealed Softmax Greedy in Many-Armed Bayesian Bandits
May 29, 2026William Overman, Mohsen BayatiMulti-Armed Bandits Exploration-Exploitation Tradeoff
Stanford University
Long Live The Balance: Information Bottleneck Driven Tree-based Policy Optimization
May 27, 2026Hao Jiang, Shurui Li, Tianpeng Bu +7RL for Language Models Exploration-Exploitation Tradeoff
Alibaba Cloud Computing, Alibaba Group.
Human Decision-Making with AI Assistance under Correlated Features
May 27, 2026Yanru Guan, Naveen Raman, Fei FangAI-Assisted Decision Making Exploration-Exploitation Tradeoff
Carnegie Mellon University
Mind the Gaps: Multi-Robot Feedback-Driven Ergodic Coverage in Unknown Environments
May 20, 2026Thales Costa Silva, Nora AyanianTrajectory Optimization Multi-Robot Systems
Department of Computer Science at Brown University, Providence-RI, USA.
Not all uncertainty is alike: volatility, stochasticity, and exploration
May 19, 2026Payam PirayMulti-Armed Bandits Exploration-Exploitation Tradeoff
Department of Psychology, University of Southern California
Uncertainty-Calibrated Recommendations for Low-Active Users
May 18, 2026Bob Junyi Zou, Sai Li, Tianyun Sun +2Uncertainty Quantification Exploration-Exploitation Tradeoff
Stanford University · Stanford, CA, USA · ByteDance Inc. +2
FML-bench: A Controlled Study of AI Research Agent Strategies from the Perspective of Search Dynamics
May 17, 2026Qiran Zou, Hou Hei Lam, Wenhao Zhao +11AI Agents for Scientific Discovery Inference-Time Search
National University of Singapore · Tsinghua University · University of Minnesota +2
Learning to Sparsify Stochastic Linear Bandits
May 11, 2026Zhengmiao Wang, Ming Chi, Zhi-Wei Liu +2Multi-Armed Bandits Exploration-Exploitation Tradeoff
School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China · Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Torino, Italy
HTPO: Towards Exploration-Exploitation Balanced Policy Optimization via Hierarchical Token-level Objective Control
May 8, 2026Xincheng Yao, Ruoqi Li, Cheng Chen +4RL for Language Models Exploration-Exploitation Tradeoff
School of Information Science and Electronic Engineering, Shanghai Jiao Tong University · Xiaohongshu Inc. · MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University.
Trading off rewards and errors in multi-armed bandits
May 1, 2026Akram Erraqabi, Alessandro Lazaric, Michal Valko +2Multi-Armed Bandits Exploration-Exploitation Tradeoff
Inria SequeL · CMU · EnLearn
Online combinatorial optimization with stochastic decision sets and adversarial losses
Apr 28, 2026Gergely Neu, Michal ValkoMulti-Armed Bandits Exploration-Exploitation Tradeoff
SequeL team, INRIA Lille – Nord Europe, France
Efficient learning by implicit exploration in bandit problems with side observations
Apr 27, 2026Tomas Kocak, Gergely Neu, Michal Valko +1Multi-Armed Bandits Exploration-Exploitation Tradeoff
SequeL team, INRIA Lille – Nord Europe, France · Current affiliation: Google DeepMind
Extreme bandits
Apr 27, 2026Alexandra Carpentier, Michal ValkoMulti-Armed Bandits Exploration-Exploitation Tradeoff
Statistical Laboratory, CMS University of Cambridge, UK · SequeL team INRIA Lille - Nord Europe, France
Resilient Decentralized Ergodic Coverage for Scalable Multi-Robot Systems in Unknown Time-Varying Environments
Apr 5, 2026Maria G. Mendoza, Victoria Marie Tuck, Chinmay Maheshwari +1Multi-Robot Systems Exploration-Exploitation Tradeoff
Department of Mechanical Engineering, University of California, Berkeley, CA 94720, USA · Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA · Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21209, USA +1
Mixture-Greedy for Online Generative Model Selection: Is UCB Necessary in Diversity-Aware Multi-Armed Bandits?
Mar 23, 2026Bahar Dibaei Nia, Farzan FarniaMulti-Armed Bandits Exploration-Exploitation Tradeoff
Department of Computer Science and Engineering, Chinese University of Hong Kong.

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Human-inspired, Task-Dimension-Guided Exploration for Efficient Learning in High Dimensions

Fanyu Zhu, Jiahui An, Ni Ji

Beijing Institute for Brain Research, Chinese Academy of Medical Sciences & Peking Union Medical College, Chinese Institute for Brain Research, Beijing, Beijing Key Laboratory of Brain Science and Brain-Machine Interface · State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University

Exploration-Exploitation Tradeoff RL Exploration Recommender Systems

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Efficient exploration in high-dimensional decision spaces remains a central challenge for decision-making systems. Humans, in contrast, can navigate large decision spaces with remarkable efficiency. Recent behavioral studies suggest that humans reduce dimensionality in large decision spaces by probing candidate feature dimensions, identifying reward-relevant ones, and restricting the effective decision space. Inspired by this mechanism, we propose TDGE (Task-Dimension-Guided Exploration), a human-inspired, model-agnostic algorithm with an automatically constructed task-dimension--feature--item hierarchy. TDGE follows a top-down exploration strategy: it first selects task-relevant feature dimensions, then identifies informative features within those dimensions, and finally recommends concrete items based on the selected features. Experiments on MovieLens-20M, LastFM, and Amazon recommendation datasets show that TDGE substantially improves exploration efficiency and cold-start adaptation over baseline algorithms. Comparisons with other structured algorithms and ablation studies attribute these gains to TDGE's hierarchical structure and semantic feature-space exploration, with robust results across clustering methods and hierarchy depths. Recommendation-trajectory visualizations also show exploration patterns similar to human dimension-guided behavior.

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