用强化学习自动清理表格数据，让模型更准更可信。
Prior-Aligned Data Cleaning for Tabular Foundation Models
- 用深度强化学习设计清洗流程，对齐真实数据与模型先验分布。
- 新奖励函数使模型在4个数据集上准确率提升至0.851，优于基准0.843。
- 跨数据集迁移有效，预训练策略比从零开始快28.8%。
表格基础模型（TFM）通过元学习合成数据生成过程，在小规模表格数据集上实现顶尖的零样本精度，对无大量标注数据的实践者极具吸引力。然而，其上下文学习机制依赖近似干净的输入：真实数据中的缺失值、异常值和重复项导致先验不匹配，同时降低准确率与置信度校准。纠正此问题需对清洗操作进行序列决策，静态规则无法预见其交互——适合强化学习（RL）。本文提出L2C2，首个将表格数据清洗建模为先验对齐的深度强化学习框架：学习策略按序执行操作，最小化脏数据与TFM合成先验之间的分布差距。十组OpenML基准测试表明：1）七种奖励设计中有三种退化为平凡清洗策略——奖励工程非简单任务；2）所提的TFMAwareReward在4/10数据集上选择结构不同的清洗路径，且在这些情况下平均准确率达0.851（基准0.843，威尔科克森p=0.063，n=4），且永不逊色；3）参数化清洗动作在9/10数据集上提升最优管道奖励（威尔科克森p=0.004）；4）在单一源数据集上预训练的策略，在三个独立测试集上均优于从零训练，在2,000步微调时已超越基准，全微调后最高提升达28.8%，证明了先验对齐知识的跨数据集迁移能力。这些结果确立先验对齐是部署TFM于真实表格数据的合理数据准备策略。
原文摘要 · Abstract (English)
Tabular Foundation Models (TFMs) achieve state-of-the-art zero-shot accuracy on small tabular datasets by meta-learning over synthetic data-generating processes -- making them highly attractive for practitioners who cannot afford large annotated corpora. However, their in-context learning mechanism assumes approximately clean inputs: missing values, outliers, and duplicates in the real-world data create a prior mismatch that degrades both accuracy and confidence calibration simultaneously. Correcting this mismatch requires sequential decisions over cleaning operators whose interactions no static preprocessing rule can anticipate -a natural fit for reinforcement learning~(RL). We introduce L2C2, the first deep RL framework framing tabular data cleaning as prior alignment: a learned policy sequences operators to minimize the distributional gap between dirty input and the TFM's synthetic prior. Six experiments on ten OpenML benchmark datasets establish: 1) three of seven reward designs collapse to degenerate trivial cleaning strategies -- principled reward engineering is scientifically non-trivial; 2) the novel TFMAwareReward reward we propose selects structurally distinct pipelines on 4/10 datasets and achieves higher TabPFN accuracy on those diverging cases (mean 0.851 vs. 0.843; Wilcoxon p=0.063, n=4) while never underperforming; 3) parameterized cleaning actions improve best-found pipeline reward on 9/10 datasets (Wilcoxon p=0.004); and 4) a policy pre-trained on one single source dataset exceeds scratch training at the 2,000-step fine-tuning checkpoint on all three held-out datasets (up to +28.8% after full fine-tuning) demonstrating cross-dataset transfer of prior-alignment knowledge. These findings establish that prior alignment is a principled data preparation strategy for TFM deployment on real-world tabular data.
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