Studying Without a Syllabus: Task-Agnostic Environment Preprocessing for LLM Agents

Forum topic · 小凯 · 2026-09-13

Summary

This post introduces an arXiv paper (2609.10824) that formalizes task-agnostic environment preprocessing for LLM agents. The key question: can an agent study an unfamiliar environment before test time, without task examples, trajectories, evaluation feedback, or knowledge of the downstream task distribution? A studying system explores an environment under a budget and produces reusable artifacts—indices, scripts, or procedural guidance—for a frozen solver. 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 stays best on the largest-corpus benchmark. Notably, larger study budgets do not reliably improve downstream reward, but studied artifacts reduce test-time sampling needed to reach a given score, shifting computation from repeated test-time attempts to a pre-task study phase.

Paper Overview

arXiv: 2609.10824
Fields: cs.AI, cs.CL, cs.LG
Authors: Vinay Samuel, Varun Ursekar, Vijay S. Kalmath, Apaar Shanker, Veronica Chatrath, Yuan Xue
Posted: 2026-09-13

Problem

The paper studies a more open-ended setting: can an agent study an unfamiliar environment *without a syllabus* — i.e., before test time and without knowledge of the downstream task distribution — and choose how to prepare it?

Contribution

task-agnostic environment preprocessing

Experiments and Findings

Compared 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; fixed corpus processing remains best on the largest-corpus benchmark.
Larger study budgets do not reliably improve downstream reward.
Studied artifacts reduce the test-time sampling needed to reach a given score — reusable preparation shifts computation from repeated test-time attempts to a pre-task study phase.

Abstract (original)

> 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. Existing task-agnostic approaches avoid this supervision but commit in advance to a preparation strategy for a particular type of environment. We study a more open-ended setting: can an agent study an unfamiliar environment without a syllabus, i.e. before test time and without knowledge of the downstream task distribution, and choose how to prepare it? We formalize task-agnostic environment preprocessing, in which a studying system explores an environment under a budget and produces artifacts for a frozen solver. We compare unaided and archive-equipped meta-agents with 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. Larger study budgets do not reliably improve downstream reward. Nevertheless, studied artifacts reduce the test-time sampling needed to reach a given score, demonstrating how reusable preparation can shift computation from repeated test-time attempts to a pre-task study phase.

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