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Tools for training models to perform multi-turn reasoning and autonomous tool use through environment feedback.
Distinguishing note: Focuses specifically on agentic trajectory optimization rather than general model fine-tuning.
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This project is a comprehensive framework for the entire lifecycle of transformer-based language models, supporting everything from foundational pretraining to specialized deployment. It provides a modular toolkit for defining neural network architectures, managing data preparation pipelines, and executing training routines across various scales. The framework is designed to handle the full model development process, including supervised fine-tuning, behavioral alignment, and the integration of agentic capabilities. What distinguishes this framework is its focus on efficient training and adva
The framework provides agentic model training to optimize trajectories for multi-turn tool use and reasoning by leveraging environment feedback and delayed rewards for autonomous tasks.