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[2602.17976] In-Context Learning for Pure Exploration in Continuous Spaces
Summary
The paper presents C-ICPE-TS, a novel algorithm for pure exploration in continuous spaces, enhancing adaptive learning strategies in machine learning applications.
Why It Matters
This research addresses the challenge of efficiently identifying hypotheses in continuous spaces, a critical aspect of machine learning that has implications for various applications, including optimization and decision-making in AI systems.
Key Takeaways
- C-ICPE-TS algorithm enables efficient exploration in continuous hypothesis spaces.
- The approach uses deep neural networks for adaptive learning without parameter updates.
- Validated across benchmarks, it shows promise in best-arm identification and function minimization.
Computer Science > Machine Learning arXiv:2602.17976 (cs) [Submitted on 20 Feb 2026] Title:In-Context Learning for Pure Exploration in Continuous Spaces Authors:Alessio Russo, Yin-Ching Lee, Ryan Welch, Aldo Pacchiano View a PDF of the paper titled In-Context Learning for Pure Exploration in Continuous Spaces, by Alessio Russo and 3 other authors View PDF HTML (experimental) Abstract:In active sequential testing, also termed pure exploration, a learner is tasked with the goal to adaptively acquire information so as to identify an unknown ground-truth hypothesis with as few queries as possible. This problem, originally studied by Chernoff in 1959, has several applications: classical formulations include Best-Arm Identification (BAI) in bandits, where actions index hypotheses, and generalized search problems, where strategically chosen queries reveal partial information about a hidden label. In many modern settings, however, the hypothesis space is continuous and naturally coincides with the query/action space: for example, identifying an optimal action in a continuous-armed bandit, localizing an $\epsilon$-ball contained in a target region, or estimating the minimizer of an unknown function from a sequence of observations. In this work, we study pure exploration in such continuous spaces and introduce Continuous In-Context Pure Exploration for this regime. We introduce C-ICPE-TS, an algorithm that meta-trains deep neural policies to map observation histories to (i) the next...
