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Studying Without a Syllabus: Task-Agnostic Environment Preprocessing

Vinay Samuel, Varun Ursekar, Vijay S. Kalmath, Apaar Shanker, Veronica Chatrath, Yuan Xue

arXiv:2609.10824v1cs.AIcs.CLcs.LG

TL;DR

The paper asks whether an agent can study an unfamiliar environment without downstream task information and choose how to prepare it. It formalizes budgeted preprocessing for a frozen solver and compares open-ended meta-agents with fixed workflows. A meta-agent leads on five of six benchmarks, while study artifacts can reduce required test-time sampling, though larger budgets do not reliably improve reward and artifacts can misdirect solvers.

  • Problem

    Existing adaptation methods commonly use task examples, trajectories, or evaluation feedback, while fixed task-agnostic methods commit to a preparation strategy; the paper asks whether an agent can study without downstream task knowledge and choose how to prepare an unfamiliar environment.

  • Method

    The paper formalizes bounded task-agnostic environment preprocessing, comparing unaided and archive-assisted meta-agents that explore environments and create reusable artifacts for a frozen solver.

  • Results

    A meta-agent variant attains the highest Avg@3 reward on five of six benchmarks, while studied artifacts reduce the test-time sampling needed to reach a given score.

  • Takeaways & Limitations

    Reusable environment preparation can shift computation from repeated test-time attempts to a pre-task study phase, but its benefits are not uniform.

  • Takeaways & Limitations

    The study uses same-family models and a shared Claude Code harness, so it cannot establish whether artifacts transfer across solvers or provide solver-independent improvements.

Abstract

from arXiv · show

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.

1 Introduction

The paper studies task-agnostic environment preprocessing, asking whether a meta-agent can explore an unfamiliar environment without downstream task information and create reusable artifacts for a frozen solver. Across heterogeneous benchmarks, open-ended studying strategies generally outperform fixed ones, while study budget does not reliably improve performance.

  • Existing adaptation methods commonly rely on task examples, trajectories, or evaluation feedback, motivating task-agnostic preparation for cold-start environments.
  • The paper formalizes preprocessing in which systems use a bounded budget without downstream tasks, traces, or labels to build reusable resources for a frozen solver.
  • Meta-agents dynamically choose how to explore an environment and what artifacts to create, with unaided and archive-assisted variants.
  • Open-ended studying strategies outperform fixed ones on five of six heterogeneous benchmarks.
  • Additional study budget does not reliably improve downstream performance, although studying can reduce the test-time compute needed to reach a given score.

2 Related Work

Related work spans task-informed adaptation, task-agnostic preprocessing, and self-generated practice. This paper positions its approach as open-ended environment-conditioned studying that can choose and combine preparation strategies rather than committing to one fixed procedure.

  • Task-informed adaptation uses task examples, evaluation signals, or trajectories to optimize prompts, harnesses, memory, and context.
  • Task-agnostic preprocessing removes downstream task information but prior work fixes the input modality or candidate interventions in advance.
  • Best@3 selects the best of three task-time rollouts and is not a fixed-cost substitute for Avg@3.
  • Practice-based systems generate reusable skills, curricula, or playbooks through self-generated interaction before target tasks.

3 Task-Agnostic Environment Preprocessing

Task-agnostic environment preprocessing lets a studying system modify an environment for a frozen solver without observing downstream task distributions, under a study-cost budget. The framework permits unrestricted preprocessing strategies and evaluates the solver on either the original or modified environment.

  • Formalization: The solver agent combines an LLM with a harness, while the environment is an isolated runtime containing resources needed to complete downstream tasks.The harness maintains context, invokes tools, and drives interaction with the environment.
  • Formalization: A benchmark defines downstream tasks as prompts paired with reward functions, and aggregate solver reward is evaluated over the benchmark’s held-out task distribution.The modified environment may be compared with the original environment at test time.
  • Formalization: A studying system maps a frozen solver and original environment to a modified environment without observing downstream task instances or distribution-derived signals.Artifacts may be supplied through the environment or existing harness context interfaces.
  • Objective: Study cost is measured in API dollars and constrained by a study budget while the system seeks to maximize expected frozen-solver reward across environments.The optimization ranges over studying systems without restricting their internal structure or logic.

4 Methods

The study methods contrast fixed preparation pipelines with open-ended meta-agents that choose how to explore environments and what artifacts to create. The methods target different environmental niches, including corpora, tools, and procedural knowledge.

  • Method comparison: Open-ended meta-agents dynamically choose both their studying procedure and artifact type, unlike fixed methods designed for predefined environmental niches.The comparison includes PREPING and CORPUS2SKILL as fixed baselines and two META-AGENT variants.
  • Fixed baselines: PREPING generates environment-grounded synthetic tasks, validates practice trajectories, and distills lessons into a procedural playbook.The playbook contains strategies, pitfalls, interface notes, and reusable code.
  • Fixed baselines: CORPUS2SKILL converts document collections into navigable skill directories using summaries, embeddings, topic clustering, hierarchy construction, and source-document indexes.Its pipeline is fixed and does not adapt its strategy to runtime feedback.
  • Open-ended studying systems: META-AGENT variants use one study agent to explore the environment and construct useful file artifacts; the archive-equipped variant can invoke, combine, or ignore seeded workflows.The unaided variant chooses without guidance, whereas the archive-equipped variant receives executable skills and descriptions.
  • Open-ended studying systems: Figure 3 compares no-study and study-assisted Best@K test-time scaling curves with Avg@3 reference levels.Best@K is an oracle upper bound for selecting among test-time rollouts.

5 Experimental Setup

The experiments evaluate four study methods and a no-study baseline across heterogeneous agentic benchmarks using a frozen solver and repeated study and downstream rollouts. Outcomes are reported with reward, study cost, inference cost, and Avg@K or Best@K aggregation.

  • Benchmarks: The benchmark suite contains six agentic environments with diverse corpora, tools, and domain knowledge, including document, file-based, and tool-oriented settings.BCP-Grep uses 830 questions over a 100,195-document corpus, while Harvey LAB uses 250 firm-knowledge tasks.
  • Solver and methods: All target-task evaluations use a frozen Claude Code solver with Claude Haiku 4.5 at temperature 1.0.The study methods and agentic components use the Claude Code harness with model assignments specified separately.
  • Study methods: The comparison includes PREPING, CORPUS2SKILL, unaided META-AGENT, archive-equipped META-AGENT, and a single NO STUDY baseline.Primary comparisons use each method’s full configuration rather than a shared study budget.
  • Repetitions and metrics: Each studying system runs independently three times per environment, and every artifact set receives three independent downstream evaluations.For APEX-Agents, study runs are performed separately for each of its 31 world environments.
  • Repetitions and metrics: Avg@K averages all K rollout rewards per task, whereas Best@K selects the best rollout; uncertainty reflects variation across study runs.The primary tables use K = 3, and NO STUDY instead uses ten independent task-time repetitions.
  • Metrics: The evaluation tracks study cost, downstream reward, and downstream inference cost, with study cost including model calls for artifact construction.Harvey LAB reports dense rubric criterion pass rate instead of its strict all-criteria-pass reward.

6 Results

Across six heterogeneous benchmarks, open-ended META-AGENT studying is the strongest method family, while archive benefits are positive but uneven. Additional study budget generally does not improve downstream performance, although studying can reduce test-time sampling needed to reach a given score.

  • A META-AGENT variant achieves the highest Avg@3 and Best@3 scores on 5 of 6 benchmarks, while the archive-equipped variant ranks first or second on every benchmark.PREPING consistently exceeds NO STUDY but never ranks first; CORPUS2SKILL is strongest on BCP-G but less reliable elsewhere.
  • Archive access improves Avg@3 by 0.046 on four benchmarks where it helps and declines by 0.021 on two, producing uneven rather than uniform gains.The largest gain is 0.129 and the largest decline is 0.023.
  • Additional study budget does not generally improve downstream performance across OfficeQA, Harvey LAB, and Apex Agents.Harvey LAB is the exception: META-AGENT W/O ARCHIVE rises from 0.271 to 0.348, while META-AGENT W/ ARCHIVE rises from 0.249 to 0.332.
  • Studying reduces the test-time sampling required to reach a given score, but its upfront cost pays off only when artifacts are reused across enough downstream tasks.Compared with studied agents, NO STUDY requires 2–8 rollouts to exceed studied Best@1 scores across the six benchmarks; this comparison excludes study cost.

7 Interpretability

The interpretability analysis finds that studying combines a shared diagnostic core with environment-specific exploration, while artifact selection and usefulness vary by environment. Broader exploration does not reliably predict reward, and artifacts can sometimes misdirect the solver.

  • 7.1 Studying Process: Broader exploration does not reliably predict quality: on OfficeQA, processing all 697 files trails methods inspecting fewer, while Harvey LAB separates methods that discover labread from those that do not.Harvey LAB scores are 0.35–0.38 when both meta-agents discover labread, versus 0.22–0.31 for methods that do not invoke it.
  • 7.1 Studying Process: Study traces combine inventory, unavailable-resource tracking, and budget-conscious extraction with environment-specific behaviors such as test anticipation and parallel delegation.Test anticipation concentrates in Apex Agents and AppWorld, while parallel delegation appears only in Harvey LAB and Apex Agents.
  • 7.1 Studying Process: Archive-assisted studying adaptively composes artifacts, but it can omit a strong standalone method, as on BCP-G where CORPUS2SKILL is strongest yet omitted.Composition varies across environments, with different combinations of open-ended-study, PREPING, and CORPUS2SKILL artifacts.
  • 7.1 Studying Process: Benefits concentrate in particular task strata rather than defining a universal notion of difficulty.On DABStep, archive-assisted studying improves reward by 0.185 on hard tasks but only 0.015 on easy tasks, where NO STUDY scores 0.792.
  • 7.2 Solver Process: Incomplete artifacts can direct the solver toward general pricing documents and away from task-specific files containing required inputs.The paper contrasts this failure with a DABStep artifact that supplies missing fraud-boundary information used correctly by the solver.

8 Discussion and Limitations

Studying is generally beneficial, but larger study budgets do not reliably improve downstream reward and artifacts can sometimes misdirect the solver. The paper’s conclusions are also limited by transfer, benchmark, archive, and cost-measurement constraints.

  • Discussion: 21 of 24 method–benchmark comparisons improve over NO STUDY, while a META-AGENT leads on five of six benchmarks and CORPUS2SKILL is strongest on BCP-G.Dynamically selecting how to study is robust but not universally optimal.
  • Discussion: Sustained study-budget scaling appears only on Harvey LAB and only for META-AGENT methods.The experiment measures how current policies respond to more budget, not what calibrated budget tracking and planning could achieve.
  • Discussion: One studied rollout reaches scores requiring two to eight NO STUDY rollouts under oracle selection, although artifacts can sometimes misdirect the solver.Studying produces reusable structure that can be amortized across future tasks, but the benefit is not guaranteed.
  • Limitations: The study cannot establish solver-independent artifact transfer because studiers and the frozen solver use models from one family and share the Claude Code harness.Six benchmarks also cannot cover every axis of agent-environment variation.
  • Limitations: The evaluation does not isolate agentic decision-making, workflow composition, individual archive entries, archive expansion, or a model-independent computation measure.Dollar cost is a provider-dependent proxy for heterogeneous computation, while FLOPs are difficult to obtain consistently for closed-source models.

9 Conclusion

The paper studies task-agnostic environment preparation, where systems inspect unfamiliar environments and construct artifacts for a frozen solver before downstream tasks are known. Across six benchmarks, a META-AGENT variant leads on five, while studying reduces test-time sampling but can sometimes misdirect the solver.

  • Conclusion: Task-agnostic environment preparation constructs artifacts for a frozen solver before the downstream task distribution is known.The artifacts may include environment resources or harness-delivered context.
  • Conclusion: The study compares fixed strategies with META-AGENT variants that study freely or compose workflows from an archive.The comparison spans six benchmarks with diverse environment resources and agent interfaces.
  • Conclusion: A META-AGENT variant attains the highest Avg@3 reward on five benchmarks, while CORPUS2SKILL is strongest on BCP-G.Archive access produces occasional large gains but not uniform improvements.
  • Conclusion: Additional study budget does not reliably improve reward, but studied artifacts reduce test-time sampling needed to reach a given score.Study artifacts can sometimes misdirect the solver.
  • Responsible Use: Task-agnostic preparation may reduce expert effort but introduces risks from exploration side effects and sensitive persisted artifacts.The paper recommends sandboxes, least-privilege access, and approval gates for exploration.

A.2 Additional Results

The additional-results material documents cost reporting, per-world META-AGENT lift analysis, benchmark environment counts, and implementation details for the shared studying methods. It also specifies synthetic-task generation and validation procedures used by PREPING.

  • Additional Results: Table 4 reports average inference cost per rollout in USD as mean ± sample SD, excluding unpriced rollouts.Studying statistics use three artifact iterations, whereas NO STUDY uses ten full task-time repetitions.
  • Additional Results: Table 5 reports average pre-task cost in USD as mean ± sample SD across three artifact-construction iterations, with NO STUDY shown as dashes.The Apex Agents CORPUS2SKILL value uses two metered iterations because one iteration retained no cost telemetry.
  • Additional Results: Figure 5 disaggregates META-AGENT W/ ARCHIVE Avg@3 lift over NO STUDY across individual Apex Agents worlds, grouped by domain.World scores macro-average tasks, and the plotted estimate averages three artifact-iteration estimates.
  • Implementation: PREPING generates synthetic QUERY and ACTION tasks, validates feasibility and completion on five-point scales, and retrieves at most 40 approved playbook entries.Its procedure emphasizes executable, natural, verifiable instructions with unambiguous answer formats.
  • Implementation: Artifact delivery exposes only each studying method’s frozen output through its native interface while preserving the original target instruction in every arm.NO STUDY makes no environment or prompt change.

B.1 Environment Exploration Analysis

The study measures exploration through files read and benchmark-specific tools invoked, then relates these behaviors to artifact composition and downstream reward. Meta-agents share diagnostic behaviors but adapt some strategies and artifact contents to each environment, while exploration breadth alone does not determine performance.

  • Measurement: Study traces measure unique files read and benchmark-specific tools invoked, excluding directory listings, filename mentions, shell operations, and generic file operations.CORPUS2SKILL is measured by documents processed rather than an interactive trace; trace-derived file counts are lower bounds.
  • Exploration and reward: Exploration breadth does not by itself determine downstream performance: file-count associations vary by benchmark, and tool-count relationships are generally weak or confounded.File slopes are nearly flat for Harvey LAB and Apex Agents, negative for OfficeQA, and positive for BCP-G; tool use is confounded with method identity on Harvey LAB.
  • Artifact composition: Archive-assisted artifacts combine outputs from multiple procedures, but their composition varies sharply across environments and can omit CORPUS2SKILL even when it is strongest standalone.CORPUS2SKILL appears in none of the BCP-G artifact sets but in every AppWorld set, both available Harvey LAB sets, and 86 of 93 Apex Agents sets.
  • Study behaviors: Environment inventory, unavailable-resource recording, and budget-conscious extraction appear in nearly all applicable traces, while anticipation and parallel delegation concentrate in selected environments.Test anticipation is concentrated in Apex Agents and AppWorld; parallel sub-agent delegation appears only in Harvey LAB and Apex Agents.
  • Artifact taxonomy: Navigation structures, method recipes, and environment constants appear in nearly every artifact set, while other artifact content varies by environment and archive assistance.Verification guidance rises from 34 of 93 unaided artifacts to all 93 archive-assisted Apex Agents artifacts.

C.1 Observed Artifact Use

The analysis estimates observed artifact use rather than causal influence because artifacts can be delivered invisibly through prompts or affect solver behavior without explicit retrieval. Within-task associations are mixed and remain observational.

  • Interpretation: No observed use does not establish no influence, and use rates should not be compared across methods because artifact delivery differs.Artifacts may be explicitly read, supplied through prompts, or acted on without visible retrieval.
  • Method: Observed-use comparisons remain observational because artifact use may follow events within a rollout, despite matching used and unused trials within tasks.Cells with fewer than 10 eligible task–iterations are omitted.
  • Within-task comparison: Observed artifact use is not uniformly associated with higher reward, with positive associations clearest for DABStep and negative contrasts on BCP-G for two methods.The reported contrasts are descriptive and do not isolate the causal effect of artifact use.

C.2 Lift by Task Stratum

Task-stratum analyses find no universal difficulty pattern: studying helps most when captured information matches downstream task structure. Examples include larger gains on hard DABStep tasks, two-application AppWorld tasks, Law tasks in Apex Agents, and document-intensive BCP-G tasks.

  • Difficulty: META-AGENT W/ ARCHIVE improves DABStep reward by 0.185 on hard tasks but only 0.015 on easy tasks, where NO STUDY already scores 0.792.Difficulty does not produce a universal pattern across benchmarks.
  • Domain: All four methods have their largest Apex Agents domain-level lift on Law, ranging from +0.089 to +0.139, while Investment Banking estimates are near zero.Management Consulting estimates are mixed, ranging from −0.017 to +0.035.
  • Retrieval strata: CORPUS2SKILL has positive BCP-G lift across all ten topic labels, rising from +0.224 with one gold document to +0.293 with four or more.The pattern is consistent with greater usefulness when tasks require locating more supporting documents, although retrieval burden is not isolated from other task differences.
  • Application count: Every studying method has its largest estimated AppWorld lift on tasks involving two applications, with reward gains from +0.108 to +0.141.The analysis uses application-count strata supplied by AppWorld.
  • Overall pattern: Studying helps when information captured during study matches the structure required by downstream tasks, rather than according to a universal difficulty pattern.This conclusion is supported by heterogeneous results across difficulty, application count, domain, and retrieval strata.
  • Helpful artifact use: A matched DABStep example shows the selected artifact succeeding in all three evaluations while NO STUDY fails all 10 after using an incorrect fraud boundary.The example is illustrative rather than a causal comparison.
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