Reward allocation, also known as the credit assignment problem, has been an important topic ineconomics, engineering, andmachine learning.

Continuous allocation tasks are a class of problems where an agent needs to distribute a limited amount of resources over a set of entities at each time step.

Research on scaling large language models (LLMs) has primarily focused on model parameters and training data size, overlooking the role of vocabulary size.

Large Language Models (LLMs) have revolutionized the field of natural language processing with their impressive capabilities.

Our analysis highlights sufficient properties for a regret minimization algorithm to be used as leader.

However, an emerging issue in allocation problems is data privacy.