Studying Without a Syllabus: Task-Agnostic Environment Preprocessing
Paper formalizes task-agnostic environment preprocessing, where agents study unfamiliar environments under a budget to build reusable artifacts for a frozen solver.
The paper formalizes task-agnostic environment preprocessing, where a studying system explores an environment under a budget and produces artifacts like indices, scripts, or procedural guidance for a frozen solver, without task examples or evaluation feedback. The authors compare unaided and archive-equipped meta-agents against fixed synthetic-practice and corpus-processing methods across six heterogeneous benchmarks. A meta-agent variant achieves the highest Avg@3 reward on five benchmarks, while fixed corpus processing remains best on the largest corpus benchmark. Studied artifacts reduce the test-time sampling needed to reach a given score, shifting computation from repeated test-time attempts to a pre-task study phase.
- Formalizes budgeted task-agnostic study producing artifacts for a frozen solver
- Meta-agent achieves highest Avg@3 reward on five of six benchmarks
- Larger study budgets do not reliably improve downstream reward
- Studied artifacts cut test-time sampling needed for a target score
From the original article
1 min read at huggingface.co
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. Most automated adaptation methods, however, rely on task examples, trajectories, or evaluation feedback to decide what to build.
By Vinay Samuel, Varun Ursekar, Vijay S. Kalmath · huggingface.co
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