DeepMath-103K is a newly released dataset designed to enhance mathematical reasoning in language models, featuring a broad range of challenging and diverse math problems. It includes rigorous decontamination processes to ensure fair evaluation, with detailed problem structures that support various research applications. The accompanying models and code are open-sourced to facilitate further exploration and development in the field.

Mini-o3 introduces an advanced system that enhances tool-based interactions for visual reasoning by supporting deep, multi-turn reasoning and achieving state-of-the-art performance on visual search tasks. The system utilizes a novel over-turn masking strategy to effectively manage response lengths during reinforcement learning, combined with a comprehensive dataset designed for exploratory reasoning. Open-source code and models are provided to facilitate reproducibility and further research.

EleutherAI has released the Common Pile v0.1, an 8 TB dataset of openly licensed and public domain text for training large language models, marking a significant advancement from its predecessor, the Pile. The initiative emphasizes the importance of transparency and openness in AI research, aiming to provide researchers with essential tools and a shared corpus for better collaboration and accountability in the field. Future collaborations with cultural heritage institutions are planned to enhance the quality and accessibility of public domain works.