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Found 9 papers, 1 papers with code
Unraveling Arithmetic in Large Language Models: The Role of Algebraic Structures
In this work, we propose that LLMs learn arithmetic by capturing algebraic structures, such as \emph{Commutativity} and \emph{Identity} properties.
RL-STaR: Theoretical Analysis of Reinforcement Learning Frameworks for Self-Taught Reasoner
This work provides a theoretical framework for understanding the effectiveness of reinforcement learning on CoT reasoning and STaR.
RAD-Bench: Evaluating Large Language Models Capabilities in Retrieval Augmented Dialogues
In real-world applications with Large Language Models (LLMs), external retrieval mechanisms - such as Search-Augmented Generation (SAG), tool utilization, and Retrieval-Augmented Generation (RAG) - are often employed to enhance the quality of augmented generations in dialogues.
Leveraging Unlabeled Data Sharing through Kernel Function Approximation in Offline Reinforcement Learning
This situation highlights the importance of finding effective ways to use unlabelled data in offline RL, especially when labelled data is limited or expensive to obtain.
CAPM: Fast and Robust Verification on Maxpool-based CNN via Dual Network
This study uses CAPM (Convex Adversarial Polytope for Maxpool-based CNN) to improve the verified bound for general purpose maxpool-based convolutional neural networks (CNNs) under bounded norm adversarial perturbations.
Sample Complexity of Kernel-Based Q-Learning
To the best of our knowledge, this is the first result showing a finite sample complexity under such a general model.
Flexible Multiple-Objective Reinforcement Learning for Chip Placement
Recently, successful applications of reinforcement learning to chip placement have emerged.
G2R Bound: A Generalization Bound for Supervised Learning from GAN-Synthetic Data
Performing supervised learning from the data synthesized by using Generative Adversarial Networks (GANs), dubbed GAN-synthetic data, has two important applications.
Representation Learning on Large and Small Data
Deep learning owes its success to three key factors: scale of data, enhanced models to learn representations from data, and scale of computation.
