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

• URL: http://arxiv.org/abs/2601.07675v1
• Date: Mon, 12 Jan 2026 16:01:49 GMT
• Title: Tab-TRM: Tiny Recursive Model for Insurance Pricing on Tabular Data
• Abstract summary: Tab-TRM is a network architecture that adapts the latent reasoning paradigm of Tiny Recursive Models (TRMs) to insurance modeling.<n>It maintains two learnable latent tokens - an answer token and a reasoning state - that are iteratively refined by a compact, parameter-efficient network.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Recursive Vision Language Models for General Symbolic Reasoning [17.777375944237015]
  Symbolic-reasoning tasks such as Sudoku, maze pathfinding, and ARC remain challenging for LLMs.<n>We propose R-Qwen, a framework built upon a pretrained Qwen backbone.<n>R-Qwen repeatedly refines a candidate solution through programmatic self-recursion and deep supervision.
  arXiv  Detail & Related papers  (2026-08-02T23:01:10Z)
• Adaptive Recurrent Message Passing for Test Time Computing on Graphs [80.56829418752513]
  We show that recurrent graph models can overcome the intrinsic mismatch between graph data and architectural designs.<n>We propose AdaR, an Adaptive Recurrent graph model, empowering flexible test-time computing on various downstream tasks without changing model parameters.
  arXiv  Detail & Related papers  (2026-06-21T12:14:11Z)
• Generative Recursive Reasoning [67.22973831501257]
  Generative Recursive reAsoning Models (GRAM) is a framework that turns latent reasoning into probabilistic multi-trajectory.<n>GRAM supports conditional reasoning via $p_(y mid x)$ and, with fixed or absent inputs, unconditional generation via $p_(x)$.
  arXiv  Detail & Related papers  (2026-05-19T05:20:56Z)
• Tiny Autoregressive Recursive Models [48.902738432071935]
  Tiny Recursive Models (TRMs) have recently demonstrated remarkable performance on ARC-AGI.<n>We propose the Autoregressive TRM and evaluate it on small autoregressive tasks.<n>Contrary to expectations, we find no reliable performance gains from the full Autoregressive TRM architecture.
  arXiv  Detail & Related papers  (2026-03-09T08:22:45Z)
• Recursive Models for Long-Horizon Reasoning [28.82044197167549]
  We show that a model can invoke itself to solve subtasks in isolated contexts.<n>We generalize our framework to modern agentic systems with arbitrary context processing and control flows.
  arXiv  Detail & Related papers  (2026-03-02T17:37:10Z)
• Generalization in Representation Models via Random Matrix Theory: Application to Recurrent Networks [7.721672385781673]
  We first study the generalization error of models that use a fixed feature representation (frozen intermediate layers) followed by a trainable readout layer.<n>We apply Random Matrix Theory to derive a closed-form expression for the generalization error.<n>We then apply this analysis to recurrent representations and obtain concise formula that characterize their performance.
  arXiv  Detail & Related papers  (2025-11-04T09:30:31Z)
• TaTToo: Tool-Grounded Thinking PRM for Test-Time Scaling in Tabular Reasoning [77.01182934427095]
  TaTToo is a novel table-grounded PRM framework that integrates tool-based verification to provide precise reward supervision.<n>We train TaTToo with a dual-stage paradigm: cold-start supervised fine-tuning to capture tool-use reasoning patterns, followed by reinforcement learning to align our model with table-based verification.
  arXiv  Detail & Related papers  (2025-10-07T17:59:41Z)
• Table-r1: Self-supervised and Reinforcement Learning for Program-based Table Reasoning in Small Language Models [52.94091440130039]
  Table reasoning (TR) requires structured reasoning over semi-structured data.<n>Small language models (SLMs) have limited capacity compared to large LMs (LLMs, e.g., GPT-4o)<n>We propose program-based TR (P-TR), which circumvents key limitations of text-based TR (T-TR) by generating executable programs.<n>Experiments on four TR benchmarks demonstrate that Table-r1 outperforms all SLM-based methods.
  arXiv  Detail & Related papers  (2025-06-06T14:52:19Z)
• Table-R1: Inference-Time Scaling for Table Reasoning [56.812846737424245]
  We develop and evaluate two post-training strategies to enable inference-time scaling.<n>For distillation, we introduce a large-scale dataset of reasoning traces generated by DeepSeek-R1.<n>For RLVR, we propose task-specific verifiable reward functions and apply the GRPO algorithm to obtain the Table-R1-Zero model.
  arXiv  Detail & Related papers  (2025-05-29T16:28:50Z)
• Train with Perturbation, Infer after Merging: A Two-Stage Framework for Continual Learning [57.514786046966265]
  We propose textbfPerturb-and-Merge (P&M), a novel continual learning framework that integrates model merging into the CL paradigm to mitigate forgetting.<n>Our proposed approach achieves state-of-the-art performance on several continual learning benchmark datasets.
  arXiv  Detail & Related papers  (2025-05-28T14:14:19Z)
• Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing [58.52119063742121]
  Retraining a model using its own predictions together with the original, potentially noisy labels is a well-known strategy for improving the model performance.<n>This paper addresses the question of how to optimally combine the model's predictions and the provided labels.<n>Our main contribution is the derivation of the Bayes optimal aggregator function to combine the current model's predictions and the given labels.
  arXiv  Detail & Related papers  (2025-05-21T07:16:44Z)
• Recurrent Reinforcement Learning with Memoroids [11.302674177386383]
  We study memory models such as Recurrent Neural Networks (RNNs) and Transformers, by mapping trajectories to latent Markov states. Neither model scales particularly well to long sequences, especially compared to an emerging class of memory models called Linear Recurrent Models. We reformulate existing models using a novel monoid-based framework that we call memoroids.
  arXiv  Detail & Related papers  (2024-02-15T11:56:53Z)
• LORE++: Logical Location Regression Network for Table Structure Recognition with Pre-training [45.80561537971478]
  Table structure recognition (TSR) aims at extracting tables in images into machine-understandable formats. We model TSR as a logical location regression problem and propose a new TSR framework called LORE. Our proposed LORE is conceptually simpler, easier to train, and more accurate than other paradigms of TSR.
  arXiv  Detail & Related papers  (2024-01-03T03:14:55Z)
• LORE: Logical Location Regression Network for Table Structure Recognition [24.45544796305824]
  Table structure recognition aims at extracting tables in images into machine-understandable formats. Recent methods solve this problem by predicting the adjacency relations of detected cell boxes. We propose a new TSR framework called LORE, standing for LOgical location REgression network.
  arXiv  Detail & Related papers  (2023-03-07T08:42:46Z)
• Measuring and Reducing Model Update Regression in Structured Prediction for NLP [31.86240946966003]
  backward compatibility requires that the new model does not regress on cases that were correctly handled by its predecessor. This work studies model update regression in structured prediction tasks. We propose a simple and effective method, Backward-Congruent Re-ranking (BCR), by taking into account the characteristics of structured output.
  arXiv  Detail & Related papers  (2022-02-07T07:04:54Z)
• An EM Approach to Non-autoregressive Conditional Sequence Generation [49.11858479436565]
  Autoregressive (AR) models have been the dominating approach to conditional sequence generation. Non-autoregressive (NAR) models have been recently proposed to reduce the latency by generating all output tokens in parallel. This paper proposes a new approach that jointly optimize both AR and NAR models in a unified Expectation-Maximization framework.
  arXiv  Detail & Related papers  (2020-06-29T20:58:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.