Large Language Models (LLMs) have successfully replicated human-like conversational abilities and demonstrated proficiency in coding. However, they continue to grapple with the challenges of maintaining high reliability and stringent abidance to ethical and safety measures. Reinforcement Learning from Human Feedback (RLHF) or Preference-based Reinforcement Learning (PbRL) has emerged as a promising solution to help fine-tune…
Reinforcement learning (RL) is a branch of artificial intelligence where an agent learns to make decisions through interaction with its environment. The principles of RL rely on concepts of agents, environments, states, actions, reward signals, policies, value functions, and a balance of exploration and exploitation.
Agents interact with their environment, which provides different states that form…
Researchers at Purdue University have presented GTX, a new system designed to efficiently manage large-scale dynamic graphs while supporting high-throughput read-write transactions and competitive graph analytics. This invention solves the issue which current transactional graph systems have with handling temporal localities and hotspots, two common features of real-world graphs.
Notably, managing such graphs is vital…
Researchers from Purdue University have unveiled a new tool, GTX, to address the challenges faced by transactional graph systems in handling large-scale graphs. GTX is designed to be highly efficient in managing dynamic graphs that feature high update arrival rates, temporal localities, and hotspots. Such capabilities are vital for applications including fraud detection, recommendation systems,…