Tencent AI Researchers Introduce Hunyuan-T1: A Mamba-Powered Ultra-Large Language Model Redefining Deep Reasoning, Contextual Efficiency, and Human-Centric Reinforcement Learning Large language models struggle to process and reason over lengthy, complex texts without losing essential context. Traditional models often suffer from context loss, inefficient handling of long-range dependencies, and difficulties aligning with human preferences, affecting the… →

NVIDIA AI Researchers Introduce FFN Fusion: A Novel Optimization Technique that Demonstrates How Sequential Computation in Large Language Models LLMs can be Effectively Parallelized Large language models (LLMs) have become vital across domains, enabling high-performance applications such as natural language generation, scientific research, and conversational agents. Underneath these advancements lies the transformer architecture, where alternating… →

This AI Paper Propose the UI-R1 Framework that Extends Rule-based Reinforcement Learning to GUI Action Prediction Tasks Supervised fine-tuning (SFT) is the standard training paradigm for large language models (LLMs) and graphic user interface (GUI) agents. However, SFT demands high-quality labeled datasets, resulting in extended training periods and high computational expenses. This dependence on extensive… →

Efficient Inference-Time Scaling for Flow Models: Enhancing Sampling Diversity and Compute Allocation Recent advancements in AI scaling laws have shifted from merely increasing model size and training data to optimizing inference-time computation. This approach, exemplified by models like OpenAI o1 and DeepSeek R1, enhances model performance by leveraging additional computational resources during inference. Test-time budget… →