# Tab-TRM: Tiny Recursive Model for Insurance Pricing on Tabular Data

arXiv: https://arxiv.org/abs/2601.07675 · HTML: https://mlsanity.com/paper/2601.07675/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 25 references that are in the map.
- First posted on arXiv 12 Jan 2026.

## Builds on (most cited first, 14 of 14)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters](https://mlsanity.com/paper/2408.03314/) (2024)
- [Show Your Work: Scratchpads for Intermediate Computation with Language Models](https://mlsanity.com/paper/2112.00114/) (2021)
- [Deep Equilibrium Models](https://mlsanity.com/paper/1909.01377/) (2019)
- [Reinforcement Learning with Verifiable Rewards Implicitly Incentivizes Correct Reasoning in Base LLMs](https://mlsanity.com/paper/2506.14245/) (2025)
- [On Embeddings for Numerical Features in Tabular Deep Learning](https://mlsanity.com/paper/2203.05556/) (2022)
- [Hierarchical Reasoning Model](https://mlsanity.com/paper/2506.21734/) (2025)
- [Entity Embeddings of Categorical Variables](https://mlsanity.com/paper/1604.06737/) (2016)
- [LocalGLMnet: interpretable deep learning for tabular data](https://mlsanity.com/paper/2107.11059/) (2021)
- [Enhancing Actuarial Non-Life Pricing Models via Transformers](https://mlsanity.com/paper/2311.07597/) (2023)
- [The Credibility Transformer](https://mlsanity.com/paper/2409.16653/) (2024)
- [In-Context Learning Enhanced Credibility Transformer](https://mlsanity.com/paper/2509.08122/) (2025)
- [Tree-like Pairwise Interaction Networks](https://mlsanity.com/paper/2508.15678/) (2025)

## Built on by (most cited first, 3 of 3)

- [Tiny Autoregressive Recursive Models](https://mlsanity.com/paper/2603.08082/) (2026)
- [Symbol-Equivariant Recurrent Reasoning Models](https://mlsanity.com/paper/2603.02193/) (2026)
- [LoopICL: Looping a single transformer block to solve tabular tasks](https://mlsanity.com/paper/2609.36108/) (2026)

## Abstract (arXiv)

We introduce Tab-TRM (Tabular-Tiny Recursive Model), a network architecture that adapts the recursive latent reasoning paradigm of Tiny Recursive Models (TRMs) to insurance modeling. Drawing inspiration from both the Hierarchical Reasoning Model (HRM) and its simplified successor TRM, the Tab-TRM model makes predictions by reasoning over the input features. It maintains two learnable latent tokens - an answer token and a reasoning state - that are iteratively refined by a compact, parameter-efficient recursive network. The recursive processing layer repeatedly updates the reasoning state given the full token sequence and then refines the answer token, in close analogy with iterative insurance pricing schemes. Conceptually, Tab-TRM bridges classical actuarial workflows - iterative generalized linear model fitting and minimum-bias calibration - on the one hand, and modern machine learning, in terms of Gradient Boosting Machines, on the other.