Large language models (LLMs) have become indispensable for various natural language processing applications, including machine translation, text summarization, and conversational AI. However, their increasing complexity and size have led to significant computational efficiency and memory consumption challenges. As these models grow, the resource demand makes them difficult to deploy in environments with limited computational capabilities.… →
Developing AI agents capable of independent decision-making, especially for multi-step tasks, is a significant challenge. DeepSeekAI, a leader in advancing large language models and reinforcement learning, focuses on enabling AI to process information, predict outcomes, and adjust actions as situations evolve. It underlines the importance of proper reasoning in dynamic settings. The new development from… →
BACKGROUND: Children with autism spectrum disorder (ASD) are known to experience difficulties in coordinating fine- and gross-motor movement. Previous interventional studies have reported significant effect of exercise-based intervention programs on improving motor skills and alleviating symptoms in ASD; however, researchers are yet to know why some participants experienced less improvement than others. One plausible explanation… →
CONCLUSIONS: Short-term reduction of FFA concentration does not improve insulin-stimulated vasodilation in patients with metabolic syndrome. →
Developing compact yet high-performing language models remains a significant challenge in artificial intelligence. Large-scale models often require extensive computational resources, making them inaccessible for many users and organizations with limited hardware capabilities. Additionally, there is a growing demand for methods that can handle diverse tasks, support multilingual communication, and provide accurate responses efficiently without sacrificing… →
Structure-from-motion (SfM) focuses on recovering camera positions and building 3D scenes from multiple images. This process is important for tasks like 3D reconstruction and novel view synthesis. A major challenge comes from processing large image collections efficiently while maintaining accuracy. Several approaches rely on the optimization of camera poses and scene geometry. However, these have… →
Large Language Models (LLMs) have become increasingly reliant on Reinforcement Learning from Human Feedback (RLHF) for fine-tuning across various applications, including code generation, mathematical reasoning, and dialogue assistance. However, a significant challenge has emerged in the form of reduced output diversity when using RLHF. Research has identified a critical trade-off between alignment quality and output… →
CONCLUSIONS: This is one of the first interventions to address FCR in FC. While acceptability of FC-FORT was good, important feasibility issues need to be addressed before moving forward with a larger randomized control trial. →
Modern AI systems rely heavily on post-training techniques like supervised fine-tuning (SFT) and reinforcement learning (RL) to adapt foundation models for specific tasks. However, a critical question remains unresolved: do these methods help models memorize training data or generalize to new scenarios? This distinction is vital for building robust AI systems capable of handling real-world… →
Post-training techniques, such as instruction tuning and reinforcement learning from human feedback, have become essential for refining language models. But, open-source approaches often fall behind proprietary models due to a lack of transparency in training data, methodologies, and optimization techniques. Despite the availability of foundational models, the absence of robust, publicly available post-training recipes creates… →