Studying Without a Syllabus: Task-Agnostic Environment Preprocessing

Before an LLM agent tackles tasks in a new environment, the environment's corpora and tools can be inspected to construct reusable resources such as indices, scripts, or procedural guidance. Most automated adaptation methods rely on task examples, trajectories, or evaluation feedback to decide what to build. Existing task-agnostic approaches avoid this supervision but commit in advance to strategies designed for particular environment types. We ask whether, before test time and without knowledge of the downstream task distribution, a meta-agent can instead study an unfamiliar environment without a syllabus and choose how to prepare it. We formalize task-agnostic environment preprocessing and compare unaided and archive-equipped meta-agents with fixed synthetic-practice and corpus-processing methods across six heterogeneous benchmarks. Studying significantly improves downstream performance in 21 of 24 method–benchmark comparisons. A meta-agent variant leads on five benchmarks, while fixed corpus processing leads on the most corpus-heavy benchmark. The same artifacts provide less consistent gains when used by a different task-solving agent. Studied artifacts can also reduce the sampling needed to reach a given score, showing how reusable preparation can shift computation from repeated test-time attempts to pre-task study.