ICML 2025 Past Other
The Exploration in AI Today Workshop at ICML 2025
EXAIT@ICML 2025
- Submission deadline
- Jun 1, 2025, 12:59 UTC imported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Auto-imported from the OpenReview venue record on 2026-06-10 — please verify and enrich (topics are keyword-guessed).
Accepted papers (51)
Fetched from OpenReview (v2) on 2026-06-10.
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A Diffusion Model to Shrink Proteins While Maintaining their Function
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Active Advantage-Aligned Online Reinforcement Learning with Offline Data
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Align While Search: Belief-Guided Exploratory Inference for Test-Time World Alignment
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Automated Data Selection for Efficient Cost Model Training to Optimize Sparse Matrix Kernels on Emerging Hardware Accelerators
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Blindfolded Experts Generalize Better: Insights from Robotic Manipulation and Videogames
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Branched Schrödinger Bridge Matching
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Central Path Proximal Policy Optimization
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Diffusion-Based Maximum Entropy Reinforcement Learning
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Direct Regret Optimization in Bayesian Optimization
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DISCOVER: Automated Curricula for Sparse-Reward Reinforcement Learning
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Distances for Markov chains from sample streams
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Diversity By Design: Leveraging Distribution Matching for Offline Model-Based Optimization
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e3: Learning to Explore Enables Extrapolation of Test-Time Compute for LLMs
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EVOLvE: Evaluating and Optimizing LLMs ForIn-Context Exploration
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Exploration by Exploitation: Curriculum Learning for Reinforcement Learning Agents through Competence-Based Curriculum Policy Search
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Fleet of Agents: Coordinated Problem Solving with Large Language Models
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Flow Density Control: Generative Optimization Beyond Entropy-Regularized Fine-Tuning
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From Words to Rewards: Leveraging Natural Language for Reinforcement Learning
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G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning
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Greed is Good: A Unifying Perspective on Guided Generation
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Improved Exploration in GFlownets via Enhanced Epistemic Neural Networks
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Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM
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In-Context Learning for Pure Exploration
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Instance-Dependent Fixed-Budget Pure Exploration in Reinforcement Learning
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Intent Factored Generation: Unleashing the Diversity in Your Language Model
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Intrinsic Benefits of Categorical Distributional Loss: Uncertainty-aware Exploration in Reinforcement Learning towards Higher Moment Regularisations
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Kevin: Multi-Turn RL for Generating CUDA Kernels
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Llama-Nemotron: Efficient Reasoning Models
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LLMs are Greedy Agents: Effects of RL Fine-tuning on Decision-Making Abilities
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No-Regret Safety: Balancing Tests and Misclassification in Logistic Bandits
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Oracle-Efficient Adversarial Reinforcement Learning via Max-Following
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Prompts Generalize with Low Data: Non-vacuous Generalization Bounds for Optimizing Prompts with More Informative Priors
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Provably Learning from Language Feedback
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Reimagining Parameter Space Exploration with Diffusion Models
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Reinforcement Learning with Action Chunking
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Reinforcement Learning with Thompson Sampling: No-Regret Performance over Finite Horizons
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Rethinking Exploration In Asynchronous Bayesian Optimization: Standard Acquisition Is All You Need
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Retrospective and Structurally Informed Exploration via Cross-task Successor Feature Similarity
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Scalable and Efficient Exploration via Intrinsic Rewards in Continuous-time Dynamical Systems
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See it to Place it: Evolving Macro Placements with Vision Language Models
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SOAPIA: Siamese-Guided Generation of Off Target-Avoiding Protein Interactions with High Target Affinity
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Sparse Optimistic Information Directed Sampling
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Stabilizing protein fitness predictors via the PCS framework
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StemCell-GPT: A Specialized AI Agent For Human Stem Cell Engineering
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Strategic Vantage Selection for Learning Viewpoint-Agnostic Manipulation Policies
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Testing LLM Understanding of Scientific Literature through Expert-Driven Question Answering: Insights from High-Temperature Superconductivity
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The Effective Horizon Challenge
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The Road Not Taken: Hindsight Exploration for LLMs in Multi-Turn RL
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Think or Not? Selective Reasoning via Reinforcement Learning for Vision-Language Models
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Toward Efficient Exploration by Large Language Model Agents
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Towards Unsupervised Multi-Agent Reinforcement Learning via Task-Agnostic Exploration