The Growing Complexity of Reasoning Data Curation Recent reasoning models, such as DeepSeek-R1 and o3, have shown outstanding performance in mathematical, coding, and scientific areas, utilizing post-training techniques like supervised fine-tuning (SFT) and reinforcement learning (RL). However, the complete methodologies…
Artificial intelligence has undergone a significant transition from basic language models to advanced models that focus on reasoning tasks. These newer systems, known as Large Reasoning Models (LRMs), represent a class of tools designed to simulate human-like thinking by producing…
In this Daytona SDK tutorial, we provide a hands-on walkthrough for leveraging Daytona’s secure sandbox environment to execute untrusted or AI-generated Python code safely within Notebook. Beginning with straightforward sandbox creation and basic code execution, the guide demonstrates how to…
Limitations of Traditional Climate Modeling Earth system models are essential tools for forecasting environmental changes and helping us prepare for the future. However, their high computational demands make it difficult to run them at resolutions fine enough for detailed, local…
Multimodal reasoning ability helps machines perform tasks such as solving math problems embedded in diagrams, reading signs from photographs, or interpreting scientific charts. The integration of both visual and linguistic information enables these systems to more closely mirror human thought…
Meta AI has introduced V-JEPA 2, a scalable open-source world model designed to learn from video at internet scale and enable robust visual understanding, future state prediction, and zero-shot planning. Building upon the joint-embedding predictive architecture (JEPA), V-JEPA 2 demonstrates…
In AI-driven development, coding agents have become indispensable collaborators. These autonomous or semi-autonomous tools can write, test, and refactor code, dramatically accelerating development cycles. However, as the number of agents working on a single codebase grows, so do the challenges:…
Introduction Large Language Models (LLMs) have shown substantial improvements in reasoning and precision through reinforcement learning (RL) and test-time scaling techniques. Despite outperforming traditional unit test generation methods, most existing approaches such as O1-Coder and UTGEN require supervision from ground-truth…
You’ve trained your machine learning model, and it’s performing great on test data. Source link
In 15 TED Talk-style presentations, MIT faculty recently discussed their pioneering research that incorporates social, ethical, and technical considerations and expertise, each supported by seed grants established by the Social and Ethical Responsibilities of Computing (SERC), a cross-cutting initiative of…
