# TLRD: Teaching LLMs to Reason over Tabular Data with Tri-Level Rationale Distillation

arXiv: https://arxiv.org/abs/2606.08295 · HTML: https://mlsanity.com/paper/2606.08295/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Prompting and chain-of-thought reasoning, one of 17 directions in LLM reasoning.
- Builds on 8 of its 36 references that are in the map.
- First posted on arXiv 6 Jun 2026.

## Placed in

- LLM reasoning / [Prompting and chain-of-thought reasoning](https://mlsanity.com/lr/prompting-and-chain-of-thought-reasoning/)

## Builds on (most cited first, 8 of 8)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [Gemma 3 Technical Report](https://mlsanity.com/paper/2503.19786/) (2025)
- [gpt-oss-120b & gpt-oss-20b Model Card](https://mlsanity.com/paper/2508.10925/) (2025)
- [UniPredict: Large Language Models are Universal Tabular Classifiers](https://mlsanity.com/paper/2310.03266/) (2023)
- [TabGemma: Text-Based Tabular ICL via LLM using Continued Pretraining and Retrieval](https://mlsanity.com/paper/2511.03570/) (2025)
- [Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding](https://mlsanity.com/paper/2508.18676/) (2025)
- [BoostLLM: Boosting-inspired LLM Fine-tuning for Few-shot Tabular Classification](https://mlsanity.com/paper/2605.06117/) (2026)

## Built on by (most cited first, 1 of 1)

- [SR-Fraud: An Outcome-Supervised Reflective LLM Agent Framework for Non-Stationary Payment Fraud Detection](https://mlsanity.com/paper/2609.27287/) (2026)

## Abstract (arXiv)

Tabular data is a primary medium for storing real-world information, driving many industrial applications of machine learning. Traditional predictors achieve strong predictive performance but do not provide readable, case-specific explanations essential for decision-making. Large Language Models (LLMs) can naturally bridge this gap by generating predictions alongside explanations. However, dataset-specific patterns, such as feature distributions and interactions, make tabular data difficult for LLMs to understand and reason over, while label-only fine-tuning improves performance at the cost of catastrophic forgetting. To address this problem, we propose Tri-Level Rationale Distillation (TLRD), a framework that converts label-only tabular datasets into structured rationale supervision for LLMs. TLRD uses a high-capacity teacher to synthesize a rationale corpus grounded in three complementary levels of evidence: instance-level feature, dataset-level distributional context, and comparison-level retrieved neighbors, then distills the rationale into student LLMs, enabling zero-overhead prediction and grounded explanation from raw features only. Experiments on multiple domain datasets show that TLRD significantly closes the performance gap between LLMs and state-of-the-art tree ensembles while producing grounded and readable explanations, offering a valuable reference for high-stakes decision-making.