DockSmith: Scaling Reliable Coding Environments via an Agentic Docker Builder

Di Qi; Fanqi Wan; Jiaran Zhang; Jieyi Hou; Liangyu Chen; Luck Ma; Mengqiang Ren; Qi Han; Xiangyu Zhang; Xin Wu

arxiv: 2602.00592 · v2 · submitted 2026-01-31 · 💻 cs.AI · cs.SE

DockSmith: Scaling Reliable Coding Environments via an Agentic Docker Builder

Jiaran Zhang , Luck Ma , Fanqi Wan , Di Qi , Xu Zhao , Jieyi Hou , Zhe Xie , Mengqiang Ren

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Xin Wu Zhewei Huang Liangyu Chen Qi Han Xiangyu Zhang

This is my paper

Pith reviewed 2026-05-16 09:14 UTC · model grok-4.3

classification 💻 cs.AI cs.SE

keywords Dockeragentic systemsenvironment constructionsoftware engineering agentstrajectory trainingSWE-benchDocker evaluationfailure recovery

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The pith

Training an agent on Docker environment construction develops transferable skills for general software engineering tasks.

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

The paper presents DockSmith as an agent specialized in building reliable Docker-based coding environments. It claims that framing this construction as a core agentic task, learned from large-scale execution-grounded trajectories, builds long-horizon reasoning and recovery abilities that extend beyond Docker work itself. A 30B model trained this way reaches new open-source highs on Docker-specific benchmarks and shows gains on out-of-distribution software engineering evaluations. This approach matters because environment setup has limited the scale of execution-grounded agent training.

Core claim

DockSmith treats environment construction not merely as preprocessing but as a core agentic capability exercising long-horizon tool use, dependency reasoning, and failure recovery. Trained on large-scale execution-grounded Docker-building trajectories from a SWE-Factory-style pipeline augmented with loop-detection and cross-task success memory, a 30B-A3B model achieves 39.72 percent Fail-to-Pass and 58.28 percent Commit Rate on Multi-Docker-Eval while improving out-of-distribution performance on SWE-bench Verified, SWE-bench Multilingual, and Terminal-Bench 2.0.

What carries the argument

The DockSmith agentic Docker builder, which generates and trains on execution-grounded trajectories using loop-detection and cross-task success memory to produce supervision that transfers beyond the construction task.

Load-bearing premise

The generated trajectories must be high-quality and diverse enough to produce genuine agentic capability transfer rather than overfitting to the synthetic data distribution.

What would settle it

If a model trained on the same trajectories shows no performance lift over a general baseline when tested on a fresh collection of Docker tasks with previously unseen dependency patterns and libraries, the transfer claim would be refuted.

read the original abstract

Reliable Docker-based environment construction is a dominant bottleneck for scaling execution-grounded training and evaluation of software engineering agents. We introduce DockSmith, a specialized agentic Docker builder designed to address this challenge. DockSmith treats environment construction not only as a preprocessing step, but as a core agentic capability that exercises long-horizon tool use, dependency reasoning, and failure recovery, yielding supervision that transfers beyond Docker building itself. DockSmith is trained on large-scale, execution-grounded Docker-building trajectories produced by a SWE-Factory-style pipeline augmented with a loop-detection controller and a cross-task success memory. Training a 30B-A3B model on these trajectories achieves open-source state-of-the-art performance on Multi-Docker-Eval, with 39.72% Fail-to-Pass and 58.28% Commit Rate. Moreover, DockSmith improves out-of-distribution performance on SWE-bench Verified, SWE-bench Multilingual, and Terminal-Bench 2.0, demonstrating broader agentic benefits of environment construction.

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.

Desk Editor's Note private letter to a colleague

Training on Docker-building trajectories gives SOTA on Multi-Docker-Eval and OOD gains, but the lack of ablations makes it hard to trust the source of the improvement.

read the letter

The punchline is that this work treats Docker environment building as a trainable agent skill and gets measurable transfer to other agent benchmarks, but the evidence for why it works is thin. They start from the observation that reliable Docker setups are a bottleneck for training execution-grounded agents. Instead of hand-crafting environments, they build an agent that does the construction, generate lots of trajectories using an augmented SWE-Factory pipeline, and train a 30B model on them. The additions are a loop-detection controller to prevent infinite loops and cross-task success memory to reuse good setups across tasks. On their Multi-Docker-Eval this gets 39.72% fail-to-pass and 58.28% commit rate, plus improvements on SWE-bench Verified, Multilingual, and Terminal-Bench. What is new is the specific pipeline for generating high-volume, execution-grounded trajectories for this sub-task and showing that the resulting model generalizes beyond Docker building. The paper does well at identifying a real infrastructure issue and providing a scalable way to address it with concrete numbers. The soft spots are around verification. There are no ablations that remove the loop detection or the memory to measure their impact, and no detailed look at whether the trajectories contain genuine long-horizon skills or just artifacts from the controllers. The stress-test concern holds up here: if the data generation process prunes certain failure modes, the model might be learning to mimic the pipeline rather than acquiring independent capability. The abstract and results don't include error analysis or human review of the data quality. This paper is for researchers and engineers working on scaling software engineering agents. Readers who care about data generation pipelines and execution feedback will get practical ideas from it. It deserves a serious referee because the problem is important and the approach is reproducible enough to evaluate. I would recommend sending it to peer review, but ask the authors to add the missing ablations and some validation of the trajectory distribution.

Referee Report

2 major / 2 minor

Summary. The paper introduces DockSmith, a specialized agentic Docker builder trained on large-scale execution-grounded trajectories generated via an augmented SWE-Factory-style pipeline that incorporates a loop-detection controller and cross-task success memory. A 30B-A3B model trained on these trajectories is reported to achieve open-source SOTA on Multi-Docker-Eval (39.72% Fail-to-Pass, 58.28% Commit Rate) with additional OOD gains on SWE-bench Verified, SWE-bench Multilingual, and Terminal-Bench 2.0, framing environment construction as a transferable agentic skill.

Significance. If the trajectory quality and attribution of gains are verified, the work would meaningfully advance scaling of execution-grounded SE agents by converting a common preprocessing bottleneck into a source of long-horizon supervision with demonstrated transfer. The OOD improvements, if robust, would strengthen the case for broader agentic capability gains from this training regime.

major comments (2)

[§4] §4 (Experimental Evaluation): The headline results on Multi-Docker-Eval and the OOD benchmarks are presented without ablation studies that isolate the contribution of the loop-detection controller or cross-task success memory in the trajectory-generation pipeline. This is load-bearing for the central claim, as the performance could reflect memorization of controller-induced biases rather than emergent agentic capability.
[§3.2] §3.2 (Trajectory Generation): No quantitative audit (human review, execution-based diversity metrics, or failure-mode analysis) of the generated trajectories is reported. Without this, it remains unclear whether the trajectories contain genuine long-horizon Docker skills or systematic artifacts from the augmentation controllers.

minor comments (2)

[Abstract] Abstract: The distinction between the base SWE-Factory pipeline and the specific loop-detection / memory augmentations could be stated more explicitly when describing the training data.
[Table 1] Table 1 (or equivalent results table): Include standard deviations or statistical significance tests alongside the reported metrics to support the SOTA claim.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments. We address each major point below and will revise the manuscript accordingly to strengthen the evidence for our claims.

read point-by-point responses

Referee: [§4] §4 (Experimental Evaluation): The headline results on Multi-Docker-Eval and the OOD benchmarks are presented without ablation studies that isolate the contribution of the loop-detection controller or cross-task success memory in the trajectory-generation pipeline. This is load-bearing for the central claim, as the performance could reflect memorization of controller-induced biases rather than emergent agentic capability.

Authors: We agree that ablation studies isolating the loop-detection controller and cross-task success memory are necessary to support the central claims. In the revised manuscript we will add these ablations: we will regenerate trajectories without each component in turn, train separate 30B-A3B models on the resulting datasets, and report performance on Multi-Docker-Eval together with the OOD benchmarks (SWE-bench Verified, SWE-bench Multilingual, Terminal-Bench 2.0). This will clarify the incremental contribution of each augmentation. revision: yes
Referee: [§3.2] §3.2 (Trajectory Generation): No quantitative audit (human review, execution-based diversity metrics, or failure-mode analysis) of the generated trajectories is reported. Without this, it remains unclear whether the trajectories contain genuine long-horizon Docker skills or systematic artifacts from the augmentation controllers.

Authors: We acknowledge that a quantitative audit of trajectory quality is currently missing. In the revision we will add a dedicated subsection reporting: (i) aggregate statistics (mean/median trajectory length, command vocabulary size, and cross-task success rate), (ii) execution-based diversity metrics (e.g., edit-distance distribution over command sequences), and (iii) a categorized failure-mode analysis derived from the pipeline logs. We will also include a small human review of 100 randomly sampled trajectories to assess the presence of genuine long-horizon Docker-building behavior. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper reports empirical results from training a 30B model on trajectories generated by an augmented SWE-Factory pipeline and measuring performance on separate held-out benchmarks (Multi-Docker-Eval, SWE-bench Verified, etc.). No equations, self-definitional reductions, fitted parameters renamed as predictions, or load-bearing self-citations are present that would make the reported metrics equivalent to the inputs by construction. The central claims remain independent measurements on external evaluation sets.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on the quality and representativeness of the synthetic trajectories generated by the augmented SWE-Factory pipeline; no new physical or mathematical axioms are introduced, but the approach implicitly assumes that execution-grounded trajectories contain transferable supervision for general agentic capabilities.

free parameters (1)

30B-A3B model size and architecture
The specific scale and mixture-of-experts configuration chosen for training; the abstract does not detail how this size was selected versus alternatives.

axioms (1)

domain assumption Execution-grounded trajectories from Docker-building attempts contain supervision that transfers to broader software engineering agent tasks
Invoked in the claim that DockSmith yields 'broader agentic benefits' beyond Docker building itself.

pith-pipeline@v0.9.0 · 5509 in / 1394 out tokens · 30441 ms · 2026-05-16T09:14:48.182698+00:00 · methodology

DockSmith: Scaling Reliable Coding Environments via an Agentic Docker Builder

Core claim

What carries the argument

Load-bearing premise

What would settle it

discussion (0)