OSU Natural Language Processing

This repository enables the generation of specialized training data for supervised fine-tuning, internal world modeling, and reflective reasoning. It focuses on creating high-quality, structured inputs that allow models to better interpret complex environments and improve their iterative decision-making processes through expert-level demonstrations.

These datasets are designed for researchers and engineers focused on advanced model training, reinforcement learning, and cognitive architecture development. Professionals working on improving model reasoning, self-correction, and environmental awareness will find these resources essential for building more robust and reliable computational systems.

Users require a foundational understanding of supervised fine-tuning methodologies and existing model training infrastructure. The datasets are provided in formats compatible with standard training pipelines, requiring familiarity with data preprocessing and model evaluation techniques to effectively integrate the expert, IWM, and reflection training samples.