Exploration-Exploitation papers, 2023–2026

Key findings

• Exploration-Exploitation went from 47 papers in 2023 to 71 in 2026, roughly 1.5x in three years.
• NeurIPS publishes the most Exploration-Exploitation work: 93 papers from 2023 to 2026, 40% of the four-venue total.
• The fastest-growing overlap is with Reasoning Models: papers tagged with both rose from 1 in 2023 to 10 in 2026.
• Among the 5 topics under Recommenders and Ads, it ranks #4 by 2026 paper count; the fastest grower in the group, Click-Through Prediction, is up 2.6x versus 1.5x here.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026Decentralized Q-Learning in Markov Potential Games
• ICML 2026Bandit Social Leaning Dynamics with Exploration Episodes
• ICLR 2026Laplacian Kernelized Bandit
• CVPR 2026Underground Plant Exploration Non-Destructive 3D Root Assessment with GPR Based
• NeurIPS 2025DUET: Dual-Perspective Pseudo Labeling and Uncertainty-aware Exploration & Exploitation Training for Source-Free Domain Adaptation
• ICML 2025Clustering Items through Bandit Feedback: Finding the Right Feature out of Many
• ICLR 2025B-STaR: Monitoring and Balancing Exploration and Exploitation in Self-Taught Reasoners
• CVPR 2025Dynamic Pseudo Labeling via Gradient Cutting for High-Low Entropy Exploration
• NeurIPS 2024Exploration, Exploitation, and Engagement in Multi-Armed Bandits with Abandonment
• ICML 2024A Contextual Combinatorial Bandit Approach to Negotiation
• ICLR 2024Dropout-Based Rashomon Set Exploration for Efficient Predictive Multiplicity Estimation
• CVPR 2024Instance-aware Exploration-Verification-Exploitation for Instance ImageGoal Navigation
• NeurIPS 2023Adaptive Algorithms for Relaxed Pareto Set Identification
• ICML 2023Curiosity in Hindsight: Intrinsic Exploration in Stochastic Environments
• ICLR 2023Combinatorial Pure Exploration of Causal Bandits
• CVPR 2023Exact-NeRF: An Exploration of a Precise Volumetric Parameterization for Neural Radiance Fields
• NeurIPS 2026Hit Expansion via Localized Exploration of Synthesizable Chemical Space
• ICML 2026Exploration-free Algorithms for Multi-group Mean Estimation
• ICLR 2026DIVERSE: Disagreement-Inducing Vector Evolution for Rashomon Set Exploration
• CVPR 2026From Exploration to Exploitation A Two-Stage Entropy RLVR Approach for

All Exploration-Exploitation papers

235 papers, page 4 of 5

151. ICML 2024Random Latent Exploration for Deep Reinforcement Learning
152. ICML 2024ReconBoost: Boosting Can Achieve Modality Reconcilement
153. ICML 2024Safe and Robust Subgame Exploitation in Imperfect Information Games
154. ICML 2024Safe Exploration in Dose Finding Clinical Trials with Heterogeneous Participants
155. ICML 2024Scalable Online Exploration via Coverability
156. ICML 2024Stochastic Bandits with ReLU Neural Networks
157. ICML 2024Straight-Through Meets Sparse Recovery: the Support Exploration Algorithm
158. ICML 2024Testing the Feasibility of Linear Programs with Bandit Feedback
159. NeurIPS 2024Bandits with Preference Feedback: A Stackelberg Game Perspective
160. NeurIPS 2024Beyond Optimism: Exploration With Partially Observable Rewards
161. NeurIPS 2024Code Repair with LLMs gives an Exploration-Exploitation Tradeoff
162. NeurIPS 2024Effective Exploration Based on the Structural Information Principles
163. NeurIPS 2024Exploration by Learning Diverse Skills through Successor State Representations
164. NeurIPS 2024Exploration, Exploitation, and Engagement in Multi-Armed Bandits with Abandonment
165. NeurIPS 2024First-Explore, then Exploit: Meta-Learning to Solve Hard Exploration-Exploitation Trade-Offs
166. NeurIPS 2024Handling Learnwares from Heterogeneous Feature Spaces with Explicit Label Exploitation
167. NeurIPS 2024Hierarchical Uncertainty Exploration via Feedforward Posterior Trees
168. NeurIPS 2024How does Inverse RL Scale to Large State Spaces? A Provably Efficient Approach
169. NeurIPS 2024Improved Analysis for Bandit Learning in Matching Markets
170. NeurIPS 2024Improved Regret of Linear Ensemble Sampling
171. NeurIPS 2024Local Anti-Concentration Class: Logarithmic Regret for Greedy Linear Contextual Bandit
172. NeurIPS 2024Nearly Minimax Optimal Regret for Multinomial Logistic Bandit
173. NeurIPS 2024NeoRL: Efficient Exploration for Nonepisodic RL
174. NeurIPS 2024No-Regret Bandit Exploration based on Soft Tree Ensemble Model
175. NeurIPS 2024Offline Oracle-Efficient Learning for Contextual MDPs via Layerwise Exploration-Exploitation Tradeoff
176. NeurIPS 2024Online Learning of Delayed Choices
177. NeurIPS 2024Online Non-convex Learning in Dynamic Environments
178. NeurIPS 2024Optimal Clustering with Bandit Feedback
179. NeurIPS 2024Optimizing the coalition gain in Online Auctions with Greedy Structured Bandits
180. NeurIPS 2024Preference-based Pure Exploration
181. NeurIPS 2024PRODuctive bandits: Importance Weighting No More
182. NeurIPS 2024Progressive Exploration-Conformal Learning for Sparsely Annotated Object Detection in Aerial Images
183. NeurIPS 2024Provably Efficient Interactive-Grounded Learning with Personalized Reward
184. NeurIPS 2024Query-Efficient Correlation Clustering with Noisy Oracle
185. NeurIPS 2024Randomized Exploration for Reinforcement Learning with Multinomial Logistic Function Approximation
186. NeurIPS 2024Rethinking Exploration in Reinforcement Learning with Effective Metric-Based Exploration Bonus
187. NeurIPS 2024Self-Guiding Exploration for Combinatorial Problems
188. NeurIPS 2024Small steps no more: Global convergence of stochastic gradient bandits for arbitrary learning rates
189. CVPR 2023Exact-NeRF: An Exploration of a Precise Volumetric Parameterization for Neural Radiance Fields
190. ICLR 2023Combinatorial Pure Exploration of Causal Bandits
191. ICLR 2023Guiding Safe Exploration with Weakest Preconditions
192. ICLR 2023Learning Achievement Structure for Structured Exploration in Domains with Sparse Reward
193. ICLR 2023Optimistic Exploration with Learned Features Provably Solves Markov Decision Processes with Neural Dynamics
194. ICLR 2023Pink Noise Is All You Need: Colored Noise Exploration in Deep Reinforcement Learning
195. ICLR 2023Replicable Bandits
196. ICLR 2023Revisiting Intrinsic Reward for Exploration in Procedurally Generated Environments
197. ICLR 2023Safe Exploration Incurs Nearly No Additional Sample Complexity for Reward-Free RL
198. ICLR 2023Which Layer is Learning Faster? A Systematic Exploration of Layer-wise Convergence Rate for Deep Neural Networks
199. ICML 2023An Information-Theoretic Analysis of Nonstationary Bandit Learning
200. ICML 2023Anti-Exploration by Random Network Distillation

FAQ

How many Exploration-Exploitation papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

235 in total: 47 in 2023, 63 in 2024, 54 in 2025, 71 in 2026.

How many Exploration-Exploitation papers are at NeurIPS 2026?

NeurIPS 2026 has 25 Exploration-Exploitation papers, versus 20 the year before.

How many Exploration-Exploitation papers are at ICLR 2026?

ICLR 2026 has 23 Exploration-Exploitation papers, versus 10 the year before.

How many Exploration-Exploitation papers are at ICML 2026?

ICML 2026 has 19 Exploration-Exploitation papers, versus 21 the year before.

How many Exploration-Exploitation papers are at CVPR 2026?

CVPR 2026 has 4 Exploration-Exploitation papers, versus 3 the year before.

Which conference publishes the most Exploration-Exploitation papers?

NeurIPS, with 93 papers from 2023 to 2026.