Browsing: LLM

Image by Editor   # Introduction  If you are trying to understand how large language model (LLM) systems actually work today, it helps to stop thinking only…

Designing algorithms for Multi-Agent Reinforcement Learning (MARL) in imperfect-information games — scenarios where players act sequentially and cannot see each other’s private information, like poker —…

NVIDIA researchers introduced ProRL AGENT, a scalable infrastructure designed for reinforcement learning (RL) training of multi-turn LLM agents. By adopting a ‘Rollout-as-a-Service’ philosophy, the system decouples…

Last year, AWS announced an integration between Amazon SageMaker Unified Studio and Amazon S3 general purpose buckets. This integration makes it straightforward for teams to use…

The scaling of Large Language Models (LLMs) is increasingly constrained by memory communication overhead between High-Bandwidth Memory (HBM) and SRAM. Specifically, the Key-Value (KV) cache size…

This post is cowritten by Paul Burchard and Igor Halperin from Artificial Genius. The proliferation of large language models (LLMs) presents a significant paradox for highly…

Image by Editor   # Introduction  AI is moving so quickly that traditional news outlets and even academic journals often struggle to keep up. LLMs, more specifically,…

According to a column by the New York Times’ Kevin Roose, employees at companies including Meta and OpenAI compete on “internal leaderboards that show how many…

In this tutorial, we build an uncertainty-aware large language model system that not only generates answers but also estimates the confidence in those answers. We implement…

If you’ve got tons of files that you constantly need to search through, you’re likely paying for software that’s reading and summarizing them under the hood.…