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

Fine-Tuning Large Language Models for Task Specific Data

Abstract: Large Language Models (LLMs) have demonstrated exceptional capabilities in natural language processing tasks. Fine-tuning techniques, however, can improve their efficiency even more for ...
IEEE

Multi-Task Learning Enhanced Physics-Informed Neural Network for Solving Fluid-Structure Interaction Equations

Abstract: Physics-informed neural networks (PINNs) incorporate physical constraints into their loss functions, allowing them to efficiently solve Partial Differential Equations (PDEs). In this work, ...