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arxiv: 2605.29559 · v1 · pith:UH4DYIXQnew · submitted 2026-05-28 · 💻 cs.CL

LiteCoder-Terminal: Scaling Long-Horizon Terminal Environments for Learning Language Agents

Xiaoxuan Peng , Kaiqi Zhang , Xinyu Lu , Boxi Cao , Yaojie Lu , Hongyu Lin , Xianpei Han , Le Sun This is my paper

Pith reviewed 2026-06-29 07:25 UTC · model grok-4.3

classification 💻 cs.CL
keywords synthetic data generationlanguage agentsterminal environmentssupervised fine-tuningpreference optimizationcommand-line workflowsmulti-step planning
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The pith

Fully synthetic executable terminal environments generated from domain specifications offer scalable verifiable training for language agents on complex command-line tasks.

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

The paper introduces a zero-dependency synthesis pipeline that creates executable and verifiable terminal environments directly from domain specifications instead of relying on scraped external repositories. Using this pipeline the authors build an SFT dataset of 11,255 expert trajectories across ten domains and an RL set of 602 environments. Fine-tuning Qwen-family models on the SFT data produces agents that reach 29.06 percent pass@1 on Terminal Bench 1.0, with further gains from Direct Multi-turn Preference Optimization on the RL environments. These results establish that synthetic environments can supply controllable, targeted supervision signals for long-horizon terminal workflows.

Core claim

A synthesis pipeline autonomously generates executable terminal training environments from domain specifications, producing LiteCoder-Terminal-SFT with 11,255 trajectories and LiteCoder-Terminal-RL with 602 environments; supervised fine-tuning and DMPO on Qwen models then yield pass@1 scores of 29.06 percent, 18.54 percent, and 34.00 percent on Terminal Bench 1.0, 2.0, and Pro.

What carries the argument

LiteCoder-Terminal-Gen, the zero-dependency pipeline that generates executable and verifiable terminal environments directly from domain specifications.

If this is right

  • Supervised fine-tuning on the generated SFT trajectories raises pass@1 rates on Terminal Bench benchmarks relative to base models.
  • Direct Multi-turn Preference Optimization on the RL environments produces further measurable gains.
  • The method removes dependence on scraped repositories while increasing domain controllability and targeting of specific capability gaps.

Where Pith is reading between the lines

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

  • The same synthesis approach could be applied to other long-horizon interactive domains such as web browsers or code interpreters.
  • Verifiable synthetic trajectories may support iterative self-improvement loops that scale beyond fixed benchmarks.

Load-bearing premise

Environments produced from domain specifications remain executable, verifiable, and representative of real terminal dynamics without introducing non-real artifacts or coverage gaps.

What would settle it

A controlled experiment in which agents trained only on the synthetic environments are evaluated on unmodified real-world terminals outside the ten specified domains and exhibit systematic failure rates.

Figures

Figures reproduced from arXiv: 2605.29559 by Boxi Cao, Hongyu Lin, Kaiqi Zhang, Le Sun, Xianpei Han, Xiaoxuan Peng, Xinyu Lu, Yaojie Lu.

Figure 1
Figure 1. Figure 1: Overview of the domain-to-task generation stage in [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 3
Figure 3. Figure 3: Domain distribution and the top-20 invoked commands in the [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Pass@k across different sampling budgets k on Terminal Bench 1.0 and 2.0 for the 4B and 30B-A3B scales. Green: base Qwen3-Instruct; blue: LiteCoder-Terminal fine-tuned on SFT trajectories. This steep scaling curve on both TB-1 and TB-2 indicates that SFT on our dataset not only improves the single-attempt pass rate (pass@1) but fundamentally enhances the agent’s latent capacity to explore and eventually un… view at source ↗
Figure 5
Figure 5. Figure 5: Cross-task evaluation on SWE￾bench. To examine whether the learned terminal-agent behav￾iors carry over to software engineering tasks, we addi￾tionally evaluate our trained models on SWE-bench. Empirical results demonstrate that the ter￾minal interaction capabilities acquired via LiteCoder-Terminal-SFT successfully general￾ize to SWE-bench. As illustrated in [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
read the original abstract

Mastering terminal environments requires language agents capable of multi-step planning, feedback-grounded execution, and dynamic state adaptation. However, training such agents is currently bottlenecked by a reliance on scraped external repositories, which limits domain diversity, environment controllability, and the targeting of specific capability deficits. We introduce LiteCoder-Terminal-Gen, a zero-dependency synthesis pipeline that autonomously generates executable and verifiable terminal training environments directly from domain specifications. Using this framework, we construct two large-scale resources: LiteCoder-Terminal-SFT, comprising 11,255 expert trajectories across 10 domains, and LiteCoder-Terminal-RL, featuring 602 verifiable environments for trajectory-level preference optimization. Supervised fine-tuning of Qwen-family models on our SFT dataset yields agents that significantly outperform their base counterparts. Notably, our 32B variant achieves 29.06%, 18.54%, and 34.00% pass@1 on Terminal Bench 1.0, 2.0, and Pro, respectively. Furthermore, applying Direct Multi-turn Preference Optimization (DMPO) on our RL environments yields additional performance gains. These results systematically demonstrate that fully synthetic, executable environments offer a scalable and verifiable supervision signal for mastering complex, real-world command-line workflows.

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 / 0 minor

Summary. The manuscript introduces LiteCoder-Terminal-Gen, a zero-dependency synthesis pipeline that generates executable terminal environments directly from domain specifications. It produces two resources: LiteCoder-Terminal-SFT (11,255 expert trajectories across 10 domains) and LiteCoder-Terminal-RL (602 verifiable environments). Supervised fine-tuning of Qwen-family models on the SFT data yields agents with pass@1 scores of 29.06%, 18.54%, and 34.00% on Terminal Bench 1.0, 2.0, and Pro (32B variant), with further gains from Direct Multi-turn Preference Optimization (DMPO) on the RL environments. The central claim is that fully synthetic, executable environments provide a scalable and verifiable supervision signal for long-horizon terminal agents.

Significance. If the generated environments accurately reproduce real terminal state transitions and error distributions, the approach could remove a major data bottleneck for training language agents on complex, multi-step command-line tasks by supplying controllable, diverse, and automatically verifiable trajectories at scale. The reported dataset sizes and benchmark gains constitute concrete empirical evidence that synthetic data can drive measurable improvements; the public release of these resources would be a clear asset for the community.

major comments (2)
  1. [Abstract] Abstract: The abstract states specific performance numbers (29.06% pass@1 etc.) and dataset sizes but supplies no description of the generation algorithm, verification procedure, benchmark protocol, or controls. This absence directly prevents evaluation of whether the environments are executable and representative, which is load-bearing for the claim that they constitute a 'verifiable supervision signal'.
  2. [Abstract] Abstract / Results: No quantitative comparison (command-frequency histograms, state-transition matrices, or error-type distributions) is reported between LiteCoder-Terminal-SFT trajectories and any real-world scraped corpus. Without such evidence, the 29–34% pass@1 gains do not establish that the synthetic data captures the failure modes required for transfer to actual command-line workflows.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We provide point-by-point responses to the major comments below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The abstract states specific performance numbers (29.06% pass@1 etc.) and dataset sizes but supplies no description of the generation algorithm, verification procedure, benchmark protocol, or controls. This absence directly prevents evaluation of whether the environments are executable and representative, which is load-bearing for the claim that they constitute a 'verifiable supervision signal'.

    Authors: We agree that the abstract could benefit from additional methodological context to support the central claims. In the revised version, we will expand the abstract to include a concise description of the LiteCoder-Terminal-Gen synthesis pipeline, the verification procedure for executability, the benchmark protocol, and key controls used. revision: yes

  2. Referee: [Abstract] Abstract / Results: No quantitative comparison (command-frequency histograms, state-transition matrices, or error-type distributions) is reported between LiteCoder-Terminal-SFT trajectories and any real-world scraped corpus. Without such evidence, the 29–34% pass@1 gains do not establish that the synthetic data captures the failure modes required for transfer to actual command-line workflows.

    Authors: The manuscript prioritizes the development of a fully synthetic pipeline to overcome limitations of scraped data, such as lack of controllability and verifiability. While we do not report direct quantitative comparisons to scraped corpora, the environments are designed to be executable and the performance on Terminal Bench (which reflects real-world terminal tasks) provides evidence of relevance. We do not believe such comparisons are necessary to support our claims and will not add them. revision: no

Circularity Check

0 steps flagged

No significant circularity; derivation is empirical and self-contained

full rationale

The paper presents an empirical pipeline: a synthesis method generates synthetic terminal environments from domain specs, produces SFT and RL datasets, trains models, and reports pass@1 gains on external Terminal Bench suites. No equations, fitted parameters, or self-citations are invoked to derive the central claim; the reported improvements are framed as measured outcomes of training rather than tautological restatements of the generation process. The absence of any load-bearing self-definition, uniqueness theorem, or renamed input makes the chain non-circular by the stated criteria.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the unelaborated assertion that domain specifications suffice to produce high-quality executable terminal environments; no free parameters or invented entities are mentioned.

axioms (1)
  • domain assumption Domain specifications can be used to generate executable and verifiable terminal environments in a zero-dependency manner.
    This premise underpins the entire LiteCoder-Terminal-Gen pipeline and the resulting datasets.

pith-pipeline@v0.9.1-grok · 5777 in / 1209 out tokens · 49071 ms · 2026-06-29T07:25:19.087860+00:00 · methodology

discussion (0)

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