用Transformer实现无需更新参数的主动探索，高效识别正确假设。
In-Context Learning for Pure Exploration
- 用Transformer从观察历史映射到查询动作与预测假设
- 在多臂赌博机和广义搜索任务中表现媲美传统自适应方法
- 适合需要快速响应的新任务，无需重新建模信息结构
我们研究主动序列假设检验，即纯探索问题：给定新任务，学习者自适应地从环境收集数据以高效确定正确假设。经典案例包括多臂赌博机中的最优臂识别（BAI），其中动作对应假设；另一重要场景是广义搜索，通过一系列有策略的查询间接揭示标签信息。本文提出上下文纯探索器（ICPE），通过元训练Transformer，将观察历史映射为查询动作与预测假设，实现上下文迁移。推理时，ICPE在新任务上主动收集证据并推断真假设，无需参数更新。在确定性、随机性和结构化基准上，包括BAI与广义搜索，ICPE表现与自适应基线相当，且无需显式建模信息结构。结果支持Transformer作为通用序列测试的实际架构。
原文摘要 · Abstract (English)
We study the problem active sequential hypothesis testing, also known as pure exploration: given a new task, the learner adaptively collects data from the environment to efficiently determine an underlying correct hypothesis. A classical instance of this problem is the task of identifying the best arm in a multi-armed bandit problem (a.k.a. BAI, Best-Arm Identification), where actions index hypotheses. Another important case is generalized search, a problem of determining the correct label through a sequence of strategically selected queries that indirectly reveal information about the label. In this work, we introduce In-Context Pure Explorer (ICPE), which meta-trains Transformers to map observation histories to query actions and a predicted hypothesis, yielding a model that transfers in-context. At inference time, ICPE actively gathers evidence on new tasks and infers the true hypothesis without parameter updates. Across deterministic, stochastic, and structured benchmarks, including BAI and generalized search, ICPE is competitive with adaptive baselines while requiring no explicit modeling of information structure. Our results support Transformers as practical architectures for general sequential testing.
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