yuraedcel28@gmail.com

yuraedcel28@gmail.com

LLMs Struggle to Act on What They Know: Google DeepMind Researchers Use Reinforcement Learning Fine-Tuning to Bridge the Knowing-Doing Gap

Language models trained on vast internet-scale datasets have become prominent language understanding and generation tools. Their potential extends beyond language tasks to functioning as decision-making agents in interactive environments. When applied to environments requiring action choices, these models are expected…

Google Researchers Introduce LightLab: A Diffusion-Based AI Method for Physically Plausible, Fine-Grained Light Control in Single Images

Manipulating lighting conditions in images post-capture is challenging. Traditional approaches rely on 3D graphics methods that reconstruct scene geometry and properties from multiple captures before simulating new lighting using physical illumination models. Though these techniques provide explicit control over light…

This AI paper from DeepSeek-AI Explores How DeepSeek-V3 Delivers High-Performance Language Modeling by Minimizing Hardware Overhead and Maximizing Computational Efficiency

The growth in developing and deploying large language models (LLMs) is closely tied to architectural innovations, large-scale datasets, and hardware improvements. Models like DeepSeek-V3, GPT-4o, Claude 3.5 Sonnet, and LLaMA-3 have demonstrated how scaling enhances reasoning and dialogue capabilities. However,…