Cover Image

Provable Benefits of In-Tool Learning for Large Language Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Houliston, Sam, Odonnat, Ambroise, Arnal, Charles, Cabannes, Vivien
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Touring sampling with pushforward maps
by: Cabannes, Vivien, et al.
Published: (2023)

Clustering Head: A Visual Case Study of the Training Dynamics in Transformers
by: Odonnat, Ambroise, et al.
Published: (2024)

Learning with Hidden Factorial Structure
by: Arnal, Charles, et al.
Published: (2024)

Easing Optimization Paths: a Circuit Perspective
by: Odonnat, Ambroise, et al.
Published: (2025)

Optimal Self-Consistency for Efficient Reasoning with Large Language Models
by: Feng, Austin, et al.
Published: (2025)

Leveraging Ensemble Diversity for Robust Self-Training in the Presence of Sample Selection Bias
by: Odonnat, Ambroise, et al.
Published: (2023)

The Galerkin method beats Graph-Based Approaches for Spectral Algorithms
by: Cabannes, Vivien, et al.
Published: (2023)

Large Language Models as Markov Chains
by: Zekri, Oussama, et al.
Published: (2024)

Learning Associative Memories with Gradient Descent
by: Cabannes, Vivien, et al.
Published: (2024)

Iteration Head: A Mechanistic Study of Chain-of-Thought
by: Cabannes, Vivien, et al.
Published: (2024)

A Hierarchical Language Model with Predictable Scaling Laws and Provable Benefits of Reasoning
by: Gaitonde, Jason, et al.
Published: (2026)

Provable Benefit of Cutout and CutMix for Feature Learning
by: Oh, Junsoo, et al.
Published: (2024)

Mode Estimation with Partial Feedback
by: Arnal, Charles, et al.
Published: (2024)

CauKer: Classification Time Series Foundation Models Can Be Pretrained on Synthetic Data
by: Xie, Shifeng, et al.
Published: (2025)

Provable Training Data Identification for Large Language Models
by: Liu, Zhenlong, et al.
Published: (2025)

Efficient RL Training for LLMs with Experience Replay
by: Arnal, Charles, et al.
Published: (2026)

Provable Long-Range Benefits of Next-Token Prediction
by: Cao, Xinyuan, et al.
Published: (2025)

Uncertainty-Penalized Direct Preference Optimization
by: Houliston, Sam, et al.
Published: (2024)

Scaling Laws for Associative Memories
by: Cabannes, Vivien, et al.
Published: (2023)

Vision Transformer Finetuning Benefits from Non-Smooth Components
by: Odonnat, Ambroise, et al.
Published: (2026)

Provable Benefit of Sign Descent: A Minimal Model Under Heavy-Tailed Class Imbalance
by: Yadav, Robin, et al.
Published: (2025)

SKADA-Bench: Benchmarking Unsupervised Domain Adaptation Methods with Realistic Validation On Diverse Modalities
by: Lalou, Yanis, et al.
Published: (2024)

Metastable Dynamics of Chain-of-Thought Reasoning: Provable Benefits of Search, RL and Distillation
by: Kim, Juno, et al.
Published: (2025)

Asymmetric REINFORCE for off-Policy Reinforcement Learning: Balancing positive and negative rewards
by: Arnal, Charles, et al.
Published: (2025)

Provable Benefits of Complex Parameterizations for Structured State Space Models
by: Ran-Milo, Yuval, et al.
Published: (2024)

Provably Robust Adaptation for Language-Empowered Foundation Models
by: Lai, Yuni, et al.
Published: (2025)

Provable Scaling Laws for the Test-Time Compute of Large Language Models
by: Chen, Yanxi, et al.
Published: (2024)

Automatic Textbook Formalization
by: Gloeckle, Fabian, et al.
Published: (2026)

Dynamic Model Predictive Shielding for Provably Safe Reinforcement Learning
by: Banerjee, Arko, et al.
Published: (2024)

Large Language Models as Tool Makers
by: Cai, Tianle, et al.
Published: (2023)

Aligning Agents like Large Language Models
by: Jelley, Adam, et al.
Published: (2024)

LoRA-One: One-Step Full Gradient Could Suffice for Fine-Tuning Large Language Models, Provably and Efficiently
by: Zhang, Yuanhe, et al.
Published: (2025)

Provably Learning Diffusion Models under the Manifold Hypothesis: Collapse and Refine
by: Huang, Wei, et al.
Published: (2026)

OR-Toolformer: Modeling and Solving Operations Research Problems with Tool Augmented Large Language Models
by: Zhang, Jianzhang, et al.
Published: (2025)

A Cost-Benefit Analysis of On-Premise Large Language Model Deployment: Breaking Even with Commercial LLM Services
by: Pan, Guanzhong, et al.
Published: (2025)

AGENT: An Aerial Vehicle Generation and Design Tool Using Large Language Models
by: Samplawski, Colin, et al.
Published: (2025)

CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning
by: Wu, Duo, et al.
Published: (2024)

Explaining Machine Learning Predictive Models through Conditional Expectation Methods
by: Ruiz-España, Silvia, et al.
Published: (2026)

Provable Failure of Language Models in Learning Majority Boolean Logic via Gradient Descent
by: Chen, Bo, et al.
Published: (2025)

Joint Embedding vs Reconstruction: Provable Benefits of Latent Space Prediction for Self Supervised Learning
by: Van Assel, Hugues, et al.
Published: (2025)