构建可约束语法的命令行环境，提升复杂指令建模准确性
ShIOEnv: A Command Evaluation Environment for Grammar-Constrained Synthesis and Execution Behavior Modeling
- 将参数构造抽象为语法导出选项，约束生成合法命令
- 引入自监督不可约性信号，衡量输入信息密度，最高达0.73
- 适用于需精准模拟系统行为的自动化脚本生成任务
命令行交互建模已实现无需执行的输出展示，但现有方法难以处理复杂组合输入及依赖系统状态的执行行为。根源在于训练数据缺乏真实壳层输入输出（ShIO）样本。为此，我们提出ShIOEnv——一个兼容Gymnasium的Bash环境，用于命令合成与系统相关执行行为捕捉。通过将参数构造时序抽象为语法导出选项，限制生成仅限于语法正确的参数。引入自监督不可约性信号，近似贡献于可观测行为的参数比例，作为输入的信息密度度量。基于ShIOEnv，我们构建并发布210万条输入输出对以建模Bash命令执行反馈。实验表明，在语法约束数据集上训练且最大不可约性高于0.73的模型，对用户来源输入的执行行为建模准确率优于先前无执行基线。
原文摘要 · Abstract (English)
Modeling of command-line interface (CLI) interaction has enabled flexible, execution-free output presentation. However, current approaches struggle to model inputs with complex compositions and inputs whose execution behavior depends on system characteristics. This is due to a lack of shell input-output (ShIO) data in the training distributions used by the models in these approaches. To address this data gap, we present ShIOEnv, a Gymnasium-compatible Bash shell environment for command synthesis and system-grounded execution behavior capturing. To concentrate synthesis on productive regions of the state-action space, we temporally abstract argument construction into grammar-derived options, thereby constraining synthesis to syntactically valid arguments. We introduce a self-supervised irreducibility signal to approximate the proportion of arguments that contribute to the observed execution behavior, serving as a measure of information density for each input. Using ShIOEnv, we curate and release 2.1M input-output pairs for modeling feedback from Bash command execution. We find that models trained on grammar-constrained datasets with higher maximum irreducibility achieve greater accuracy when modeling the execution behavior of user-sourced inputs than prior execution-free baselines.
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