GitHub

Scaling Agentic Reinforcement Learning with a Multi-Turn, Multi-Task Framework

For a minimal example of how to use the environment framework, refer to examples/simple-calculator. For the environment and training data used in our paper, see AgentBench FC. For reproducing the ...

True agentic AI is years away - here's why and how we get there

Today's AI agents are a primitive approximation of what agents are meant to be. True agentic AI requires serious advances in reinforcement learning and complex memory.
IEEE

An Enhanced HCR-Based Approach To Cognitive Task Analysis

Abstract: Cognitive task analysis methods have been extensively applied across various fields since the 1980s. Among these, the GOMS (Goals, Operators, Methods, Selection rules) model stands out as ...
Hosted on MSN

Supervised learning example explained with real-life use case

What is supervised learning and how does it work? In this video/post, we break down supervised learning with a simple, real-world example to help you understand this key concept in machine learning.
techxplore

Enabling small language models to solve complex reasoning tasks

As language models (LMs) improve at tasks like image generation, trivia questions, and simple math, you might think that human-like reasoning is around the corner. In reality, they still trail us by a ...
IEEE

Multi-UAV Dynamic Task Assignment Based on Event-Triggered Graph Reinforcement Learning Under Weak Communication

Abstract: This paper addresses the dynamic task assignment problem for multiple uncrewed aerial vehicles (UAVs) operating under weak communication. Existing learning-based methods face two primary ...