SoftPipe: A Soft-Guided Reinforcement Learning Framework for Automated Data Preparation

• URL: http://arxiv.org/abs/2507.13710v2
• Date: Sat, 26 Jul 2025 10:45:43 GMT
• Title: SoftPipe: A Soft-Guided Reinforcement Learning Framework for Automated Data Preparation
• Authors: Jing Chang, Chang Liu, Jinbin Huang, Shuyuan Zheng, Rui Mao, Jianbin Qin,
• Abstract summary: We introduce SoftPipe, a novel RL framework that replaces rigid hard constraints'' with a flexible soft guidance'' paradigm.<n>We demonstrate that SoftPipe achieves up to a 13.9% improvement in pipeline quality and 2.8$times$ faster convergence compared to existing methods.
• Score: 10.764970149373845
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Data preparation is a foundational yet notoriously challenging component of the machine learning lifecycle, characterized by a vast combinatorial search space. While reinforcement learning (RL) offers a promising direction, state-of-the-art methods suffer from a critical limitation: to manage the search space, they rely on rigid ``hard constraints'' that prematurely prune the search space and often preclude optimal solutions. To address this, we introduce SoftPipe, a novel RL framework that replaces these constraints with a flexible ``soft guidance'' paradigm. SoftPipe formulates action selection as a Bayesian inference problem. A high-level strategic prior, generated by a Large Language Model (LLM), probabilistically guides exploration. This prior is combined with empirical estimators from two sources through a collaborative process: a fine-grained quality score from a supervised Learning-to-Rank (LTR) model and a long-term value estimate from the agent's Q-function. Through extensive experiments on 18 diverse datasets, we demonstrate that SoftPipe achieves up to a 13.9\% improvement in pipeline quality and 2.8$\times$ faster convergence compared to existing methods.

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