mlsanityData to 14 Oct 2026

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

arXiv 2507.13710 ↗First posted 18 Jul 2025cs.DB cs.LGcited by 0

On the map: Deep reinforcement learning methods → RL training of language and agent models

Abstract (arXiv)

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× faster convergence compared to existing methods.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL training of language and agent models, one of 15 directions in Deep reinforcement learning methods. Builds on 13 of its 28 references that are in the map. First posted on arXiv 18 Jul 2025.

Builds on

13 of 28 references.

References · 13

  1. 2025Qwen3 Technical Reportcited by 8,820
  2. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  3. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 8,065
  4. 2014OpenML: networked science in machine learningcited by 526
  5. 2018Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Reviewcited by 482
  6. 2017Feature Engineering for Predictive Modeling using Reinforcement Learningcited by 78
  7. 2016Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Sciencecited by 75
  8. 2025Advancing Multimodal Reasoning: From Optimized Cold Start to Staged Reinforcement Learningcited by 53
  9. 2024Efficient Reinforcement Learning with Large Language Model Priorscited by 30
  10. 2019DeepLine: AutoML Tool for Pipelines Generation using Deep Reinforcement Learning and Hierarchical Actions Filteringcited by 21
  11. 2023DiffPrep: Differentiable Data Preprocessing Pipeline Search for Learning over Tabular Datacited by 9
  12. 2025CausalCOMRL: Context-Based Offline Meta-Reinforcement Learning with Causal Representationcited by 3
1 more
  1. 2025CleanSurvival: Automated data preprocessing for time-to-event models using reinforcement learningcited by 2

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2025LLaPipe: LLM-Guided Reinforcement Learning for Automated Data Preparation Pipeline Construction
  2. 2026Data-dependent Exploration for Online Reinforcement Learning from Human Feedback
  3. 2026A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions
  4. 2026GEM: Guided Expectation-Maximization for Behavior-Normalized Candidate Action Selection in Offline RL
  5. 2025Guiding Exploration in Reinforcement Learning Through LLM-Augmented Observations
  6. 2026ToolSearcher: Optimizing Tool Selection at Scale via Reinforcement Learning
  7. 2023The In-Sample Softmax for Offline Reinforcement Learning
  8. 2025DeepSearch: Overcome the Bottleneck of Reinforcement Learning with Verifiable Rewards via Monte Carlo Tree Search