Vision-Language Action (VLA) models have enabled language-driven robotic manipulation by integrating language instructions, visual perception, and action generation. However, existing VLA approaches ...

The current microgrids are experiencing growing difficulties in voltage stability and operational capacity, particularly with constant power loads (CPLs), leading to negative impedance behavior and ...

Abstract: A precise world model is imperative for the performance of Model-Based Reinforcement Learning (MBRL). Active exploration enhances world models via repeatedly visiting uncertain regions where ...

Deep Reinforcement Learning (DRL) algorithms combine artificial neural networks with reward-based learning processes, and are a useful analog of reward-based information processing and dopamine-driven ...

Chattanooga, TN - (January 07, 2025) - Heil®, part of Environmental Solutions, a Terex® brand, announces its Common Body platform, an engineering advancement that brings together two of the industry’s ...

How can a small model learn to solve tasks it currently fails at, without rote imitation or relying on a correct rollout? A team of researchers from Google Cloud AI Research and UCLA have released a ...

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 ...

Large language models have made impressive strides in mathematical reasoning by extending their Chain-of-Thought (CoT) processes—essentially “thinking longer” through more detailed reasoning steps.

This repository presents Reinforcement Learning Guidance (RLG), an innovative inference-time method designed to enhance and control the alignment of diffusion models. RLG builds upon the widely used ...