A systematic data curation pipeline enables agentic models to be trained generalizably across diverse task types while achieving competitive or superior results compared to specialized models.
Successful domain specialization of LLMs requires careful tuning of learning rate, data-mixing ratios, and checkpoint selection to avoid catastrophic forgetting.
