TFM-Retouche: A Lightweight Input-Space Adapter for Tabular Foundation Models

TL;DR

This paper introduces TFM-Retouche, a lightweight, architecture-agnostic input-space adapter for tabular foundation models that improves task adaptation efficiency and performance.

Contribution

It proposes a novel residual input-space adapter that enhances pretrained models without extensive fine-tuning or architecture-specific PEFT methods.

Findings

TFM-Retouche achieves top performance on TabArena-Lite with minimal tuning.

It improves predictive accuracy and calibration over frozen models.

The method maintains efficiency with low training and inference costs.

Abstract

Tabular foundation models (TFMs), such as TabPFN-2.6, TabICLv2, ConTextTab, Mitra, LimiX, and TabDPT, achieve strong zero-shot performance through in-context learning, but their inductive biases remain fixed at inference time. Adapting a pretrained TFM to a specific dataset or task typically requires either full fine-tuning, which is computationally expensive, or parameter-efficient tuning methods (PEFT) such as LoRA, which must be tailored to the internal architecture of each TFM. Furthermore, the evidence on whether weight-space fine-tuning improves accuracy or calibration is mixed \citep{tanna_exploring_2026,rubachev_finetuning_2025}. We introduce TFM-Retouche, a lightweight input-space residual adapter that is architecture-agnostic by design with respect to the frozen TFM backbone. TFM-Retouche learns a small residual correction in the input space to align the input data with the inductive biases of the pretrained model. The adapter is trained end-to-end through the frozen TFM, with a post-training identity guard that falls back to the unmodified TFM whenever adaptation does not help on held-out validation. On TabArena-Lite (51 datasets spanning binary classification, multiclass classification, and regression), TabICLv2-Retouche -- the framework instantiated on TabICLv2 -- is the top-ranked method on the leaderboard with light per-task tuning and ensembling, lifting aggregate Elo by +56 over the frozen TabICLv2 base and sitting on the Pareto front of predictive quality versus both training and inference time.