In Large language models(LLM), developers and researchers face a significant challenge in accurately measuring and comparing the capabilities of different chatbot models. A good benchmark for evaluating these models should accurately reflect real-world usage, distinguish between different models’ abilities, and regularly update to incorporate new data and avoid biases. Traditionally, benchmarks for large language models,…
Traditional methods for training vision-language models (VLMs) often require the centralized aggregation of vast datasets, which raises concerns regarding privacy and scalability. Federated learning offers a solution by allowing models to be trained across a distributed network of devices while keeping data locally but adapting VLMs to this framework presents unique challenges. To address these…
Reinforcement learning (RL) is a type of learning approach where an agent interacts with an environment to collect experiences and aims to maximize the reward received from the environment. This usually involves a looping process of experience collecting and enhancement, and due to the requirement of policy rollouts, it is called online RL. Both on-policy…