Task decomposition is a fundamental mechanism in program synthesis, enabling complex problems to be broken down into manageable subtasks. ExeDec, a state-of-the-art program synthesis framework, employs this approach by combining a Subgoal Model for decomposition and a Synthesizer Model for program generation to facilitate compositional generalization. In this work, we develop REGISM, an adaptation of ExeDec that removes decomposition guidance and relies solely on iterative execution-driven synthesis. By comparing these two exemplary approaches-ExeDec, which leverages task decomposition, and REGISM, which does not-we investigate the interplay between task decomposition and program generation. Our findings indicate that ExeDec exhibits significant advantages in length generalization and concept composition tasks, likely due to its explicit decomposition strategies. At the same time, REGISM frequently matches or surpasses ExeDec's performance across various scenarios, with its solutions often aligning more closely with ground truth decompositions. These observations highlight the importance of repeated execution-guided synthesis in driving task-solving performance, even within frameworks that incorporate explicit decomposition strategies. Our analysis suggests that task decomposition approaches like ExeDec hold significant potential for advancing program synthesis, though further work is needed to clarify when and why these strategies are most effective.

翻译：任务分解是程序综合中的基本机制，能够将复杂问题拆解为可管理的子任务。ExeDec作为先进的程序综合框架，通过结合用于分解的子目标模型和用于程序生成的综合模型，采用此方法以促进组合泛化。在本研究中，我们开发了REGISM，这是ExeDec的一个变体，它移除了分解指导，仅依赖迭代执行驱动的综合。通过比较这两种典型方法——利用任务分解的ExeDec和不利用任务分解的REGISM——我们探究了任务分解与程序生成之间的相互作用。我们的研究结果表明，ExeDec在长度泛化和概念组合任务中表现出显著优势，这很可能归因于其明确的分解策略。同时，REGISM在各种场景中经常达到或超越ExeDec的性能，其解决方案往往更接近真实分解。这些发现突显了重复执行引导的综合在推动任务解决性能中的重要性，即使在包含明确分解策略的框架内也是如此。我们的分析表明，像ExeDec这样的任务分解方法在推进程序综合方面具有巨大潜力，但需要进一步研究来阐明这些策略何时以及为何最为有效。