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…
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…
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…
The rapid advancement of Large Language Models (LLMs) has significantly improved their ability to generate long-form responses. However, evaluating these responses efficiently and fairly remains a critical challenge. Traditionally, human evaluation has been the gold standard, but it is costly, time-consuming, and prone to bias. To mitigate these limitations, the LLM-as-a-Judge paradigm has emerged, leveraging…
Agentic AI stands at the intersection of autonomy, intelligence, and adaptability, offering solutions that can sense, reason, and act in real or virtual environments with minimal human oversight. At its core, an “agentic” system perceives environmental cues, processes them in light of existing knowledge, arrives at decisions through reasoning, and ultimately acts on those decisions—all…
Knowledge Tracing (KT) plays a crucial role in Intelligent Tutoring Systems (ITS) by modeling students’ knowledge states and predicting their future performance. Traditional KT models, such as Bayesian Knowledge Tracing (BKT) and early deep learning-based approaches like Deep Knowledge Tracing (DKT), have demonstrated effectiveness in learning student interactions. However, recent advancements in deep sequential KT…