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Recursive Environment Scaling for General Long-Horizon Agents

Lingxiao Jing, Zhengbo Jiao, Yichen Xiang, Bofan Zhu, Lingbei Meng, Yan Ni, Haorui Chen, Ran Ran, Zhirong Shen, Yuheng Yang, Chenhang Cui, Yaosong Du, Rui Huang

arXiv:2610.16397v1cs.AI

TL;DR

Agent benchmarks can differ in how information, resources, and action dependencies are organized, raising the question of how to scale executable environments without changing task objectives. RES recursively transforms and validates existing environments while retaining accepted changes. Across the reported evaluations, scaling lowers task success, while RES trajectories improve SFT across all nine model–benchmark comparisons.

  • Problem

    Agent task difficulty depends on information organization, resource connections, and how earlier actions constrain later ones, motivating the question of how to scale executable environments while preserving core objectives.

    Source: §1 · p. 2

  • Method

    RES has a proposer transform existing benchmark environments, execution-grounded checks validate candidates, and accepted changes are inherited across rounds for paired evaluation and trajectory collection.

    Source: §1 · pp. 2–3

  • Results

    Task success falls in all 18 comparisons across six models and three benchmarks, including a DeepSeek-V4.1-Flash drop from 82.86% to 5.71% on Claw-Eval-Live.

    Source: §1 · p. 2

  • Takeaways & Limitations

    The results support environment scaling as a practical way to extend existing benchmarks and collect training trajectories that improve agent performance.

    Source: §6 · p. 10

Abstract

from arXiv · show

We study environment scaling as a way to increase agent-benchmark difficulty while holding core task objectives fixed. Our framework, Recursive Environment Scaling (RES), transforms existing benchmark environments while preserving their objectives and completion requirements. A proposer changes information access, resource relations, and execution dependencies; execution-grounded checks validate the resulting candidate, and accepted changes are inherited across rounds. This produces related environments for paired evaluation and training-trajectory collection. Experiments on 294 initial tasks from three benchmarks show lower task success for all six evaluation models, with decreases of 57.14--77.14 percentage points on Claw-Eval-Live. With equal trajectory counts and identical optimization settings, fine-tuning on RES trajectories outperforms original-trajectory SFT in all nine model--benchmark comparisons by 7.00--14.61 percentage points. Ablations identify recursive inheritance and dependency composition as important contributors to increased difficulty. Together, these results support environment scaling for both agent evaluation and training.

1 Introduction

The introduction frames environment structure as a way to scale agent evaluation while preserving task objectives. It introduces RES and reports that scaled environments reduce success and RES trajectories improve fine-tuning outcomes.

1 Introduction

  • Across digital environments, task difficulty depends on information organization, resource connections, and how earlier actions constrain later ones, motivating environment scaling.The introduction asks how executable environments can be scaled recursively while preserving core task objectives.Source: §1 · p. 2
  • Unlike approaches that construct tasks or environments, RES transforms existing benchmark environments under fixed objectives and completion requirements.Prior work includes task synthesis, executable-policy construction, recursive solution extension, and sandbox or environment construction.Source: §1 · p. 2
  • RES reduces task success in all 18 comparisons across six models and three benchmarks; DeepSeek-V4.1-Flash falls from 82.86% to 5.71% on Claw-Eval-Live.The introduction also reports that RES trajectories improve all three Qwen3.5 Base models over original-trajectory SFT on every benchmark.Source: §1 · pp. 2–3
  • RES transforms existing benchmark environments while preserving core objectives, then validates and recursively inherits accepted changes for paired evaluation and trajectory collection.A proposer changes information access, resource relations, and execution dependencies; target-agent evaluation informs stopping separately from environment acceptance.Source: §1 · pp. 2–3, Figure 3 · p. 3

2 Related Work

Related work spans executable-environment evaluation, task and trajectory construction, and environment construction or adaptation. RES is distinguished by recursively transforming existing benchmarks while retaining their core requirements.

Evaluating agents in executable environments

  • Executable-environment benchmarks cover repository, terminal, website, enterprise, and multi-application tasks, with some emphasizing execution evidence and file dependencies.Examples include SWE-bench, Terminal-Bench, WebArena, WorkArena, AppWorld, Claw-Eval-Live, and Workspace-Bench.Source: §2 · p. 3

Task and trajectory construction

  • Task and trajectory work ranges from collected human action sequences to deeper tasks, executable-policy verification, executed traces, and recursively extended solutions.RES instead keeps core task requirements fixed while transforming the environment.Source: §2 · p. 3

Environment construction and adaptation

  • Environment construction includes packaged runtimes, interactive sandboxes, tools and dependency graphs built from scratch, and jointly trained environment designers; RES recursively inherits changes to existing benchmarks.The resulting lineages support paired difficulty evaluation and training-trajectory collection.Source: §2 · p. 3

3 Method

RES fixes each task’s objective and completion requirements while transforming its executable environment. Validation gates acceptance, and accepted children become parents for further transformations.

3.1 Problem Formulation and Overview

  • A task is represented by its objective and constraints alongside an executable environment; RES preserves the objective, underlying facts, and completion requirements as a validated lineage begins.Specification updates describe the access instructions needed after environmental changes.Source: §3.1 · p. 4

3.2 Environment Transformation

  • Transformation plans bind resource, relational, and operational changes to concrete resources, while dependency composition can add prerequisites and extend execution paths around the same objective.Later transformations can build on inherited dependency structures while retaining a valid solution path.Source: §3.2 · p. 4

3.3 Validated Environment Construction

  • Acceptance checks task preservation, construction consistency, and public executability by replaying transformations and checking recovered content, references, and prerequisites.Candidates with missing records, invalid references, or unsatisfied prerequisites are rejected; failed checks can trigger repair while the accepted parent remains available.Source: §3.3 · p. 4

3.4 Recursive Environment Scaling

  • Each validated child becomes the next parent, so later plans reuse inherited access paths and attach prerequisites to earlier operations.RES evaluates original and accepted environments and stops recursion at a budget or stopping condition, retaining the last accepted snapshot.Source: §3.4 · p. 5

4 Experiments

Experiments compare paired original and RES-scaled environments across three benchmarks and six models, and compare RES-trajectory SFT with original-trajectory SFT under matched training settings. Scaled environments reduce task success, while RES trajectories improve fine-tuning results.

4.1 Experimental Setup

  • The study tests three benchmarks with 105, 100, and 89 tasks, using paired original and last-accepted environments and six evaluation models.Evaluation uses one 50-turn attempt per task, with tool permissions and scoring held fixed within each pair; training compares three Qwen3.5 Base models under equal trajectory counts and identical optimization settings.Source: §4.1 · pp. 5–6

4.3 Training on Scaled Environments

  • RES-trajectory SFT outperforms original-trajectory SFT in all nine model–benchmark comparisons, with average gains of 10.16, 10.00, and 10.86 points across the three benchmarks.The largest reported gains for 35B-A3B are 12.38, 14.00, and 14.61 points, respectively; the table compares the two SFT conditions.Source: Table 2 · p. 6, §4.3 · p. 6

4.2 Scaling Benchmark Difficulty

  • Task success falls in all model comparisons across three benchmarks, including a 82.86%-to-5.71% drop for DeepSeek-V4.1-Flash on Claw-Eval-Live.The reported reductions are 12.00–19.00 percentage points on Workspace-Bench-Lite and 41.57–47.19 on Terminal-Bench 2.1.Source: §4.2 · p. 6
  • After scaling, every model has lower Claw native score and Workspace RPR, indicating fewer completed requirements as well as fewer completed tasks.For GPT-5.6 Sol, Claw native score falls from 91.24 to 35.44; Workspace RPR reductions range from 5.52 to 10.81 points.Source: §4.2 · p. 6

4.3 Training on Scaled Environments

  • RES-trajectory training also improves partial completion: for the 4B model, Workspace RPR rises from 26.59 to 33.68 and TCR@80 from 4.00 to 11.00.The gains span rubric-level completion and the number of tasks meeting the success threshold.Source: §4.3 · pp. 6–7

5 Ablations and Analysis

The analyses show that recursive scaling raises interaction demand while reducing task success and partial completion, and that dependency composition, inheritance, depth, and library breadth contribute to stronger enhancement. Construction costs vary across recursive stages, while a reconciliation case study traces added interaction to inherited access and recovery steps around an unchanged objective.

5.1 Ablation Studies

  • Removing cross-operator references and preparation prerequisites raises pass@1 from 0.0% to 100.0% and mean native score from 7.73 to 92.93.Mean turns fall from 50.0 to 19.1; component comparisons share 15 tasks, with one attempt per environment and a 50-turn limit.Source: §5.1 · p. 7
  • 6.7% versus 93.3%: recursive construction yields lower pass@1 than independent enhancements under matched nominal proposer-call limits per enhancement.Scores are 14.40 versus 88.73, and recursive construction uses approximately 2.5 times the mean target-token expenditure.Source: §5.1 · p. 7
  • 82.9% to 4.8%: pass@1 falls from E0 to depth ten, while mean native score falls from 87.84 to 13.37.The same 105 lineages were measured with early-stopped finals retained; pass@1 was 60.0% after one round and 17.1% at depth two.Source: §5.1 · p. 7
  • 6.7% to 80.0%: restricting transformations to a Basic subset raises pass@1 relative to the shared regenerated control.Native score rises from 14.40 to 85.73; recursion and planned depths remain unchanged, while the full library offers more ways to compose environmental requirements.Source: §5.1 · p. 8

5.2 Cumulative Effects of Recursive Environment Scaling

  • 82.86% to 18.18%: historical mean success falls from budget zero to ten, while mean native score declines from 0.878 to 0.260.These benchmark-level historical means average each task’s retained measurements over budgets zero through d and then average equally across tasks.Source: §5.2 · p. 8, Figure 4 · p. 8
  • 3.55 to 43.22: mean turns rise from budget zero to ten, tool calls rise from 8.05 to 206.02, and the turn-limit rate reaches 75.06%.Measurements include successful and unsuccessful trajectories; depth and endpoint evaluations use separate batches.Source: §5.2 · p. 8, Figure 4 · p. 8

5.3 Environment Scaling Efficiency and Cost

  • 92.0% of 289 accepted children require at most two outer proposer executions, at a mean cost of 1.45 executions, 89.2k tokens, and 112.6 seconds per child.Figure 5 shows fewer outer executions but greater token use at later stages; reported costs exclude unsuccessful branches, target solving, and grading.Source: §5.3 · p. 8, Figure 5 · p. 9

5.4 Case Study: Three-Source Financial Reconciliation

  • 171 versus three tool calls: in the paired reconciliation evaluation, DeepSeek-V4.1-Flash’s native score falls from 0.98 to zero without completing the report.The added work is concentrated in evidence acquisition through inherited access and recovery dependencies, while the reconciliation objective and deliverable remain fixed.Source: §5.4 · pp. 9–10

6 Conclusion

The conclusion presents RES as a way to make existing executable environments controllable for evaluation and training while preserving core task objectives. It reports lower task completion across benchmarks and models, and improved performance from RES-collected training trajectories.

6 Conclusion

  • RES makes existing executable environments a controllable dimension of agent evaluation and training while preserving core task objectives.The conclusion connects lower task completion across three benchmarks and six models with improved results for all three Qwen3.5 Base models over original-trajectory SFT.Source: §6 · p. 10

AI Use Statement

  • Generative AI tools assisted with manuscript migration, language and presentation, consistency checks, bibliographic metadata, and build verification; authors reviewed changes and retained responsibility.The authors state that they made the scientific and editorial decisions.Source: AI Use Statement · p. 10

A Evaluation and Environment Scaling

The appendix specifies how scaling, validation, independent evaluation, and construction costs are measured. It also reports a manual review of matched environments and summarizes the review findings.

A Evaluation and Environment Scaling

  • Scaling-stage attempts determine when recursion stops, depth reevaluation measures changes over saved lineages, and paired comparisons evaluate original and RES-scaled environments.Independent Claw-Eval-Live success counts native scores of at least 0.80; scaling-stage stopping uses complete fulfilment.Source: Table A1 · p. 13, A Evaluation and Environment Scaling · p. 13
  • Claw validation replays declared operators from the frozen parent, checks protected-contract hashes and rebuilt files, and rejects content loss, invalid references, nonterminating preparation, or original-file bypasses.Stopping requires three assessed failures with no measurement errors.Source: A Evaluation and Environment Scaling · p. 13
  • Three researchers reviewed 60 task lineages and 120 endpoint environments across the three benchmarks; none of the checked issues was found in the reviewed samples.Review checks covered objectives, required information, scoring conditions, answer leakage, and scoring bypasses, alongside programmatic checks.Source: A Evaluation and Environment Scaling · p. 14
  • The 289 accepted children account for 420 outer executions, 25.78M input and output tokens, and 9.04 hours of summed proposer execution time.Construction costs exclude branches producing no child, target solving, grading, and orchestration overhead.Source: A Evaluation and Environment Scaling · p. 14, Table A2 · p. 14

B Training Data and Optimization

The training comparison uses equal-sized trajectory sets with matched optimization settings and evaluates resulting checkpoints on original benchmark tasks. The appendix describes the selected original-trajectory data, training procedure, and overlap checks.

B Training Data and Optimization

  • Both SFT conditions start from the same Base checkpoint and use 8,428 trajectories with identical optimization settings; evaluation uses the original environments of 294 benchmark tasks.Original trajectories come from ClawGym and were generated by DeepSeek-V4.1-Flash; RES trajectories are newly collected in scaled environments using the same selection and supervision rules.Source: B Training Data and Optimization · p. 14, Table A3 · p. 15
  • The selected original-trajectory set contains 8,428 distinct tasks, including 3,381 full-score and 5,047 partial-score trajectories.Each task contributes one structurally complete rollout with a released native score of at least 0.8; the set contains 190.52M input tokens and 39.74M supervised target tokens.Source: B Training Data and Optimization · p. 14
  • Training preserves available reasoning, tool calls, tool outputs, and final responses, while loss applies to assistant reasoning, responses, and unmasked tool calls.Calls with missing parameters or unverified semantics remain in context but are excluded from the loss.Source: B Training Data and Optimization · pp. 14–15
  • The authors run one epoch of full-parameter BF16 SFT on four H800 GPUs and evaluate the final checkpoint without a held-out validation split.The same optimization settings apply at each model size and in both training conditions.Source: B Training Data and Optimization · p. 15, Table A3 · p. 15
  • Overlap checks found no matches between training tasks and the 105 Claw-Eval-Live, 100 Workspace-Bench-Lite, and 89 Terminal-Bench 2.1 evaluation tasks under the checks performed.Claw and Workspace checks included identifiers, task text, input-content hashes, and lexical similarity.Source: B Training Data and Optimization · p. 15

C Recursive Evaluation and Aggregation

The recursive evaluation retains stopped environments and summarizes performance from both current-state and historical views. At budget ten, the depth batch’s current-state success differs from the separate endpoint batch’s result.

C.1 Current and Historical Statistics

  • The analysis reevaluates 394 distinct environments from 105 Claw-Eval-Live seeds, using one 50-turn attempt per environment.For a stopped lineage, the last accepted environment is retained at later budgets.Source: §C.1 · p. 15
  • The historical mean averages each task’s retained measurements from budget zero through d, then averages equally across tasks.At budget two, each task’s mean uses measurements from budgets zero, one, and two; stopped lineages reuse their final measurement.Source: §C.1 · pp. 15–16
  • At budget ten, current-state success is 4.76%; the separate endpoint batch reports 5.71%.The current-state and historical statistics both keep all 105 seeds, including stopped lineages.Source: §C.1 · p. 16

C.2 Interaction Demand at Selected Budgets

The depth analysis reports performance at cap ten alongside interaction demand across recursion budgets. Its retained-state aggregation includes lineages that stopped before the allowed budget.

C.2 Interaction Demand at Selected Budgets

  • Only 1 of 105 lineages reaches depth ten, so higher-budget summaries combine many stopped lineages’ final environments with the few that continue.All 105 seeds are retained as the allowed recursion budget increases.Source: §C.2 · p. 16
  • Table A4 covers 105 tasks at each budget and reports pass rate, native score, turns, tools, and tokens; Figure A3 compares interaction demand at selected budgets.Historical interaction statistics average each task’s retained measurements from budgets zero through d.Source: §C.2 · p. 16, Table A4 · p. 17, Figure A3 · p. 17

C.3 Performance Across Depth Budgets

  • At cap ten, this batch records 5/105 passes and mean score 0.134, versus 6/105 and 0.142 in the separate endpoint-comparison batch.Each batch independently evaluates original environments under the single-attempt, 50-turn protocol.Source: §C.3 · p. 17

D.1 Settings and Interventions

The component analyses compare RES controls with interventions that alter dependencies, recursive inheritance, or the transformation library. They also report paired per-task scores and target execution costs.

D.1 Settings and Interventions

  • The component study selects 15 complete cases from 20 frozen lineages; selection uses endpoint availability and planned depths, not target scores.Basic retains six operators, while Full contains 17; both use the same runtime, verifier, proposer settings, and planned task depths.Source: §D.1 · pp. 17, 19
  • Dependency removal resolves cross-operator references into usable inputs and releases cross-resource prerequisites while retaining task instructions, facts, and transformation histories.Model-free restoration checks verify environment consistency before target evaluation.Source: §D.1 · p. 18
  • The inheritance comparison gives recursive and independent construction arms 66 opportunities each, with neither using target feedback during construction.Each independent attempt starts from the original environment; the recursive arm evaluates the inherited endpoint.Source: §D.1 · p. 18

D.2 Per-task Results

  • Table A5 reports native scores for 15 selected tasks, while Figure A5 pairs each intervention with its RES control in fixed task order.The table distinguishes saved and rebuilt RES controls, dependency removal, independent construction, and the six-operator Basic variant.Source: §D.2 · p. 19, Table A5 · p. 19, Figure A5 · p. 20

D.3 Execution Cost and Score Sensitivity

  • Every saved RES task reaches the 50-turn limit; removing dependencies reduces the mean to 19.1 turns.The component comparisons comprise 90 rollouts, including failed and turn-limited attempts.Source: §D.3 · p. 19

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