Studying Without a Syllabus: Task-Agnostic Environment Preprocessing
Field: NLP Authors: Vinay Samuel, Varun Ursekar, Vijay S. Kalmath Published: 2026-09-11 arXiv: 2509.05820
Summary
Before an LLM agent tackles tasks in a new environment, it can inspect available corpora and tools and construct reusable resources such as indices, scripts, or procedural guidance. However, 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 a preparation strategy for a particular type of environment.
This paper studies a more open-ended setting: can an agent study an unfamiliar environment without a syllabus—that is, before test time and without knowledge of the downstream task distribution—and choose how to prepare it? The authors formalize task-agnostic environment preprocessing, in which a studying system explores an environment under a budget and produces artifacts for a frozen solver.
Key findings
- The evaluation compares unscaffolded and dossier-equipped meta-agents against fixed synthetic-exercise and corpus-processing baselines across six heterogeneous benchmarks.
- Meta-agent variants achieve the highest Avg@3 reward on five of the six benchmarks; fixed corpus processing remains best on the benchmark with the largest corpus.
- Larger studying budgets do not reliably improve downstream reward.
- Nevertheless, learned artifacts reduce the test-time sampling needed to reach a given score, demonstrating that reusable preparation shifts computation from repeated test attempts into a pre-task learning phase.