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Key-value caching, as an optimization technique, focuses on improving the efficiency of the inference process in Large Language Models(LLMs) by reusing previously computed states. In simple terms, it’s a way for the model to “remember” previous calculations to avoid re-computing them for every new word it generates.
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In today’s rapidly evolving artificial intelligence landscape, one mathematical concept stands as a cornerstone across countless applications: the Kullback-Leibler (KL) divergence. With the rise of large language models (Thanks to ChatGPT!), we’ve been fastforwarded into the future, thanks to the countess innovations that has gone in the past. From training large language models to detecting anomalies in real-time data streams, KL divergence has become the silent engine powering some of the most sophisticated AI systems. As we witness unprecedented advances in machine learning, understanding this fundamental measure of distributional difference has never been more crucial for data scientists and ML researchers.
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Hello World! Welcome to my first blog post. Today, I’m diving into a topic that’s shaping the future of AI — Reinforcement Learning (RL). Whether you’re an AI enthusiast, a curious reader, or just someone wondering how robots and algorithms are getting so smart, this post is for you.