LLMs have shown advancements in reasoning capabilities through Reinforcement Learning with Verifiable Rewards (RLVR), which relies on outcome-based feedback rather than imitating intermediate reasoning steps. Current RLVR works face critical scalability challenges as they heavily depend on manually curated collections of questions and answers for training. As reasoning models advance, constructing large-scale, high-quality datasets becomes… →

Computer science research has evolved into a multidisciplinary effort involving logic, engineering, and data-driven experimentation. With computing systems now deeply embedded in everyday life, research increasingly focuses on large-scale, real-time systems capable of adapting to diverse user needs. These systems often learn from massive datasets and must handle unpredictable interactions. As the scope of computer… →

AI models today are expected to handle complex tasks such as solving mathematical problems, interpreting logical statements, and assisting with enterprise decision-making. Building such models demands the integration of mathematical reasoning, scientific understanding, and advanced pattern recognition. As the demand for intelligent agents in real-time applications, like coding assistants and business automation tools, continues to… →

Multimodal AI rapidly evolves to create systems that can understand, generate, and respond using multiple data types within a single conversation or task, such as text, images, and even video or audio. These systems are expected to function across diverse interaction formats, enabling more seamless human-AI communication. With users increasingly engaging AI for tasks like… →

OpenAI has launched Reinforcement Fine-Tuning (RFT) on its o4-mini reasoning model, introducing a powerful new technique for tailoring foundation models to specialized tasks. Built on principles of reinforcement learning, RFT allows organizations to define custom objectives and reward functions, enabling fine-grained control over how models improve—far beyond what standard supervised fine-tuning offers. At its core,… →