HeteroRL is a novel heterogeneous reinforcement learning framework designed for stable and scalable training of large language models (LLMs) in geographically distributed, resource-heterogeneous ...

Abstract: Reinforcement learning (RL) typically presupposes instantaneous agent-environment interactions, but in real-world scenarios such as robotic control, overlooking observation delays can ...

Introduction: The learning process is characterized by its variability rather than linearity, as individuals differ in how they receive, process, and store information. In traditional learning, taking ...

We propose TraceRL, a trajectory-aware reinforcement learning method for diffusion language models, which demonstrates the best performance among RL approaches for DLMs. We also introduce a ...

Abstract: The intelligent antijamming algorithm based on deep reinforcement learning (DRL) has become a prominent focus in communication antijamming research. However, while DRL aims to accurately fit ...