LLM Training

LLM training refers to the process of building or improving a large language model by optimizing its parameters on large text and, optionally, multimodal datasets. It includes dataset selection and preparation, objective definition, running pretraining and fine-tuning (e.g., supervised fine-tuning), and iterative evaluation. Additional steps such as alignment (e.g., preference optimization) and safety and quality checks are often integrated to achieve desired behavior, robustness, and compliance. Effective LLM training requires reproducible pipelines, clear metrics, controlled experimentation, and awareness of risks such as data leakage, bias, hallucinations, and cost.