Training Methods for Large Language Models: Current Approaches and Challenges

Large Language Models (LLMs) have emerged as a dominant paradigm in natural language processing, demonstrating strong performance across a wide range of generation and reasoning tasks. These systems depend on multi-stage training pipelines that integrate large-scale self-supervised pre-training, supervised fine-tuning, and alignment techniques. This paper presents a systematic mapping study of contemporary LLM training methodologies, emphasizing transformer-based architectures, optimization objectives, and data curation strategies as well as emerging sparse architectures such as Mixture-of-Experts (MoE) models. We analyze parameter-efficient fine-tuning approaches, retrieval-augmented generation frameworks, and multimodal training techniques, which we organize into a unified comparative taxonomy. We discuss key technical challenges such as scalability constraints, hallucination, bias amplification, and alignment–capability tradeoffs, then identify emerging research directions such as reasoning-centric training. This work provides a concise technical reference for researchers and practitioners working on scalable and reliable language model training.