TFM-Retouche: A Lightweight Input-Space Adapter for Tabular Foundation Models
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-spac...
| Authors | Duong Nguyen, Mohammed Jawhar, Nicolas Chesneau |
|---|---|
| Primary category | cs.LG |
| Published | 7 May 2026 |
| Last updated at source | 8 May 2026 |
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