Research papers
Submissions describing reinforcement learning methods applied to real-world experimental systems.
Machine Learning/CORE Unranked
RL4XS Workshop 2026
Reinforcement Learning for Experimental Sciences Workshop
The RL4XS Workshop 2026, held at NeurIPS in Paris, explores how reinforcement learning can bridge the simulation-to-reality gap in experimental sciences. It brings together ML researchers and experimental scientists to advance adaptive experimentation, efficient discovery, and safe, human-in-the-loop systems in fields like medicine, agriculture, and particle physics.
Paper fit
Contribution paths
A strong submission should clearly identify its contribution and evaluate it appropriately.
System descriptions
Detailed accounts of systems or platforms enabling RL in experimental sciences.
Benchmarks
Proposals of simulation environments, digital twins, or benchmarking platforms for RL in experimental science.
Position papers
Opinion or perspective pieces on the role of RL in experimental sciences.
Research areas in scope
01
Research Topics
RL for Experimental PlatformsBridging the Sim-to-Real GapEfficient and Safe LearningField & Clinical ExperimentsHuman-in-the-Loop ScienceBenchmarks & Infrastructure
Policies worth checking twice
- Submissions must follow the NeurIPS 2026 main conference paper template.
- Main text is limited to nine content pages, including all figures and tables.
- References, optional technical appendices, and mandatory paper checklist do not count toward the page limit.
- Maximum submission size is 50 MB.
- Reviewers are not obliged to review supplementary material.
- The review process is double-blind; all submissions must be anonymized and no identification information may be leaked.
- Each submission receives at least three reviews, followed by a discussion phase.
- Accepted contributions will be presented as posters, with a subset selected for spotlight presentations.
Official sources
Compiled from the official call for papers. The organizers’ pages remain authoritative.
Last verified September 9, 2026