How do modern alignment objectives combine multiple cost functions in LLMs?

LLM alignment combines supervised, preference-based, and reinforcement objectives into a unified training pipeline.

Modern LLM alignment pipelines combine multiple cost functions: supervised fine-tuning loss, preference optimization loss (DPO or RLHF), and regularization terms like KL divergence to prevent distribution drift. These objectives compete, requiring careful weighting to maintain fluency, helpfulness, and safety simultaneously.