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arxiv: 2601.05808 · v2 · submitted 2026-01-09 · 💻 cs.CL · cs.AI· cs.LG

EnvScaler: Scaling Tool-Interactive Environments for LLM Agent via Programmatic Synthesis

Xiaoshuai Song , Haofei Chang , Guanting Dong , Yutao Zhu , Ji-Rong Wen , Zhicheng Dou This is my paper

Pith reviewed 2026-05-16 16:01 UTC · model grok-4.3

classification 💻 cs.CL cs.AIcs.LG
keywords LLM agentstool interactionenvironment synthesisprogrammatic generationsupervised fine-tuningreinforcement learningmulti-turn interactionstrajectory validation
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The pith

EnvScaler uses programmatic synthesis to create hundreds of tool-interaction environments and thousands of validated scenarios for training LLM agents.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper proposes EnvScaler as an automated way to build rich training sandboxes for LLM agents that must use multiple tools across several turns. It addresses the shortage of scalable environments by first mining topics and modeling logic to create environment skeletons, then generating task scenarios along with rule-based functions that validate correct trajectories. The resulting collection of 191 environments and roughly 7,000 scenarios is used for both supervised fine-tuning and reinforcement learning on the Qwen3 model family. Experiments on three benchmarks show clear gains in the models' ability to solve tasks that require coordinated, multi-step tool use. A sympathetic reader would care because current agent training is bottlenecked by access to realistic, consistent interaction data, and this method offers a path to expand that data without manual construction or simulation artifacts.

Core claim

EnvScaler constructs diverse environment skeletons through topic mining, logic modeling, and quality evaluation, then generates multiple task scenarios and rule-based trajectory validation functions for each skeleton. This process produces 191 environments and about 7,000 scenarios that are applied to supervised fine-tuning and reinforcement learning of Qwen3 series models. The resulting agents demonstrate significantly stronger performance on three benchmarks that measure success in complex environments requiring multi-turn, multi-tool interactions.

What carries the argument

SkelBuilder for constructing environment skeletons and ScenGenerator for producing task scenarios together with rule-based trajectory validators.

If this is right

  • Agents trained this way can complete more multi-step tasks that require coordinated calls to several tools.
  • The rule-based validators provide reliable reward signals during reinforcement learning without external simulation errors.
  • The same synthesis pipeline can be rerun to produce additional environments whenever new tool APIs become available.
  • Training data volume scales linearly with compute rather than human effort, enabling larger agent models to be fine-tuned.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Validation functions created during synthesis could be reused at inference time to detect when an agent deviates from valid trajectories.
  • The approach might extend to other agent domains such as web navigation or code execution if similar logic models can be defined.
  • Because the environments are fully specified by code, they allow systematic variation of difficulty parameters to study scaling laws for agent performance.

Load-bearing premise

The programmatically generated environments and validation rules accurately reflect real-world tool behaviors and supply consistent training signals without hidden inconsistencies.

What would settle it

Training an LLM agent on the synthesized set and then measuring no improvement (or a decline) in success rate on held-out real-world multi-turn tool-use tasks compared with a baseline trained without them.

Figures

Figures reproduced from arXiv: 2601.05808 by Guanting Dong, Haofei Chang, Ji-Rong Wen, Xiaoshuai Song, Yutao Zhu, Zhicheng Dou.

Figure 1
Figure 1. Figure 1: An illustration of tool-interactive environ [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The overview of EnvScaler. with the pass rate indicating environment quality. To further synthesize multiple task scenarios for each environment, we propose ScenGenerator. To ensure task relevance and solvability within a given environment and scenario, ScenGenerator first syn￾thesize the environment’s initial database/state, and then derives challenging tasks from the current state. To achieve rule-based … view at source ↗
Figure 3
Figure 3. Figure 3: The overall framework of SkelBuilder. 2025). In this paper, we focus on general tool use across various domain-specific environments (Patil et al., 2025; Yao et al., 2025; Chen et al., 2025), rather than tool-integrated reasoning and web in￾formation access centered on Python or search tools (Dong et al., 2025; Li et al., 2025a). Some work have explored the training data and RL strate￾gies from different p… view at source ↗
Figure 4
Figure 4. Figure 4: The overall framework of ScenGenerator. Item Avg. Med. # Constraint Rules Per Env 4.58 5 # State Category Per Env Level 1 (e.g., user, message, item) 3.74 4 Level 2 per Level 1 (e.g., u_id, u_phone) 5.72 5 Total 21.38 21 # Tools Per Env Env Information Query (e.g., list_users) 10.44 10 Env State Change (e.g., send_message) 8.14 8 Total 18.58 18 [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Pairwise comparison of different LLMs on a [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: The RL training and validation curve of Qwen3 in synthetic environments after SFT. gains across all datasets, whereas Qwen3-1.7B shows notable improvements on BFCL-MT and ACEBench-Agent but a slight drop on Tau-Bench. The main reason is that large-scale models pos￾sess stronger exploration capabilities during RL, enabling them to extract effective strategies. In contrast, small-scale models, with weaker fo… view at source ↗
Figure 7
Figure 7. Figure 7: The change of Qwen3-4B’s performance with [PITH_FULL_IMAGE:figures/full_fig_p008_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Diversity and statistical distributions of 191 synthesized environments. [PITH_FULL_IMAGE:figures/full_fig_p011_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: The Direct RL training and validation curve [PITH_FULL_IMAGE:figures/full_fig_p013_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: The prompt for filtering tasks situated within a domain-specific, stateful environment. [PITH_FULL_IMAGE:figures/full_fig_p014_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: The prompt for inferring environment description from existing tasks. [PITH_FULL_IMAGE:figures/full_fig_p014_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: The prompt for planning state and rules of the environment. [PITH_FULL_IMAGE:figures/full_fig_p015_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: The prompt for planning tool operations of the environment. [PITH_FULL_IMAGE:figures/full_fig_p015_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: The prompt for programmatically converting states into the class definition. [PITH_FULL_IMAGE:figures/full_fig_p015_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: The prompt for programmatically converting operation to class-method. [PITH_FULL_IMAGE:figures/full_fig_p016_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: The prompt for initializing the testing agent during environments assessment. [PITH_FULL_IMAGE:figures/full_fig_p016_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: The prompt for initializing the checking agent during environments assessment. [PITH_FULL_IMAGE:figures/full_fig_p017_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: The prompt for generating environment’s initial state data. [PITH_FULL_IMAGE:figures/full_fig_p017_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: The prompt for generating a task under the specific environment and state. [PITH_FULL_IMAGE:figures/full_fig_p018_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: The prompt for generating verification checklist for a task. [PITH_FULL_IMAGE:figures/full_fig_p018_20.png] view at source ↗
Figure 21
Figure 21. Figure 21: The prompt for programmatically converting each checkpoint to a verification function. [PITH_FULL_IMAGE:figures/full_fig_p018_21.png] view at source ↗
Figure 22
Figure 22. Figure 22: The system prompt for prompting LLM agents under the [PITH_FULL_IMAGE:figures/full_fig_p019_22.png] view at source ↗
Figure 23
Figure 23. Figure 23: The system prompt for prompting LLM agents under the [PITH_FULL_IMAGE:figures/full_fig_p019_23.png] view at source ↗
Figure 24
Figure 24. Figure 24: The system prompt for prompting the LLM to act as a user under the [PITH_FULL_IMAGE:figures/full_fig_p019_24.png] view at source ↗
Figure 25
Figure 25. Figure 25: An example of initial state data configuration for the environment. [PITH_FULL_IMAGE:figures/full_fig_p027_25.png] view at source ↗
Figure 26
Figure 26. Figure 26: An example task under the above state configuration. [PITH_FULL_IMAGE:figures/full_fig_p028_26.png] view at source ↗
read the original abstract

Large language models (LLMs) are expected to be trained to act as agents in various real-world environments, but this process relies on rich and varied tool-interaction sandboxes. However, access to real systems is often restricted; LLM-simulated environments are prone to hallucinations and inconsistencies; and manually built sandboxes are hard to scale. In this paper, we propose EnvScaler, an automated framework for scalable tool-interaction environments via programmatic synthesis. EnvScaler comprises two components. First, SkelBuilder constructs diverse environment skeletons through topic mining, logic modeling, and quality evaluation. Then, ScenGenerator generates multiple task scenarios and rule-based trajectory validation functions for each environment. With EnvScaler, we synthesize 191 environments and about 7K scenarios, and apply them to Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) for Qwen3 series models. Results on three benchmarks show that EnvScaler significantly improves LLMs' ability to solve tasks in complex environments involving multi-turn, multi-tool interactions. We release our code and data at https://github.com/RUC-NLPIR/EnvScaler.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper introduces EnvScaler, a framework for programmatically synthesizing scalable tool-interactive environments for training LLM agents. It features SkelBuilder, which uses topic mining, logic modeling, and quality evaluation to create environment skeletons, and ScenGenerator, which produces task scenarios and rule-based trajectory validators. The authors generate 191 environments and approximately 7,000 scenarios, apply them to SFT and RL training of Qwen3 models, and claim significant improvements on three benchmarks involving multi-turn, multi-tool interactions.

Significance. If the synthesized environments accurately capture real-world tool interactions without systematic artifacts, EnvScaler could substantially advance scalable training of LLM agents by reducing reliance on manual construction or restricted real-system access. The public release of code and data is a clear strength for reproducibility.

major comments (2)
  1. [Abstract and §4] Abstract and §4 (Experiments): The central claim of 'significant improvements' on three benchmarks is stated without any quantitative metrics, baseline comparisons, error bars, or statistical details. This is load-bearing because the reported gains cannot be assessed for magnitude, significance, or whether they exceed what would be expected from training on any large synthetic dataset.
  2. [§3.1 and §3.2] §3.1 (SkelBuilder) and §3.2 (ScenGenerator): No quantitative fidelity metrics are reported (e.g., distribution distance to real tool-use logs, expert realism ratings, or execution mismatch rates against live APIs). The rule-based validators alone do not address the risk that benchmark gains reflect overfitting to synthetic simplifications rather than transferable multi-turn tool-use capability.
minor comments (1)
  1. [Abstract] The abstract uses the imprecise phrase 'about 7K scenarios'; the exact count and breakdown by environment should be stated.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below and will revise the manuscript to strengthen the quantitative presentation of results and add fidelity analyses for the synthesized environments.

read point-by-point responses

  1. Referee: [Abstract and §4] Abstract and §4 (Experiments): The central claim of 'significant improvements' on three benchmarks is stated without any quantitative metrics, baseline comparisons, error bars, or statistical details. This is load-bearing because the reported gains cannot be assessed for magnitude, significance, or whether they exceed what would be expected from training on any large synthetic dataset.

    Authors: We agree that the abstract summarizes results qualitatively and that §4 would benefit from additional statistical details. The experiments section already contains tables comparing EnvScaler-trained models against baselines including the base Qwen3, standard SFT, and prior agent methods, with concrete accuracy and success-rate improvements on the three benchmarks. In the revision we will (1) insert key quantitative figures into the abstract, (2) add error bars from multiple random seeds and p-values for statistical significance in §4, and (3) include a new control experiment training on an equivalently sized unstructured synthetic dataset to isolate the contribution of EnvScaler’s structured synthesis. revision: yes

  2. Referee: [§3.1 and §3.2] §3.1 (SkelBuilder) and §3.2 (ScenGenerator): No quantitative fidelity metrics are reported (e.g., distribution distance to real tool-use logs, expert realism ratings, or execution mismatch rates against live APIs). The rule-based validators alone do not address the risk that benchmark gains reflect overfitting to synthetic simplifications rather than transferable multi-turn tool-use capability.

    Authors: We acknowledge that explicit fidelity metrics are absent from the submitted version. The rule-based validators guarantee executability, and SkelBuilder’s quality evaluation enforces logical coherence, yet these do not quantify distributional similarity to real tool-use data. In the revision we will add: (i) embedding-based distribution distances between generated scenarios and held-out real tool-use traces, (ii) execution mismatch rates measured against live APIs on a sampled subset, and (iii) an analysis showing that benchmark gains persist on tasks whose tool sequences differ from the synthetic training distribution. Full expert human ratings would require a new study; we will therefore report the above automated metrics and note the limitation. revision: partial

Circularity Check

0 steps flagged

No significant circularity in synthesis-to-training pipeline

full rationale

The paper's core chain is: (1) SkelBuilder performs topic mining + logic modeling + quality evaluation to produce environment skeletons; (2) ScenGenerator adds rule-based trajectory validators to produce scenarios; (3) the resulting 191 environments / ~7K scenarios are used as training data for SFT and RL on Qwen3 models; (4) performance is measured on three external benchmarks. None of these steps is defined in terms of its own outputs, no parameters are fitted on a subset and then relabeled as predictions, and no load-bearing premise rests on a self-citation whose content is itself unverified. The synthesis pipeline is presented as an independent, programmatic process whose fidelity is asserted rather than derived from the downstream benchmark numbers. Consequently the reported improvements are not forced by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Review is based on abstract only; no explicit free parameters, axioms, or invented entities are described in the provided text. The synthesis process likely relies on implicit choices in topic mining and logic modeling, but these are not specified.

pith-pipeline@v0.9.0 · 5519 in / 1165 out tokens · 63000 ms · 2026-05-16T16:01:07.104146+00:00 · methodology

discussion (0)

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Forward citations

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Reference graph

Works this paper leans on

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    all possible data in an e-commerce system

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    success": False,

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