Structured Reasoning with Large Language Models

Research on reasoning processes is still under progress, Large Language Models (LLMs) have demonstrated remarkable natural language processing capacity recently. Emphasizing multi-step problem-solving, organized decision-making, and human feedback alignment, this paper critically reviews eight foundational works supporting the evolution of LLM reasoning. This paper investigates how generative pre-training (GPT-1, GPT-2) supports unsupervised and zero-shot learning after building parallelizable and scalable self-attention mechanisms with the Transformer architecture. Thanks to the invention of Chain-of- Thought (CoT) prompting, which demonstrated that sequential thinking increases logical coherence, LLMs can now study numerous paths of reasoning. Tree of Thoughts (ToT) later grew out from this. Reinforcement Learning from Human Feedback (RLHF) has been essential in improving LLM alignment beyond prompting strategies; Prototypical Reward Models (Proto-RM) improve the efficacy of learning from human preferences. Retrieval-Augmented Thought Trees (RATT) also solve the problem of factual consistency by including outside knowledge sources; Thought Space Explorer (TSE) increases cognitive exploration and lets LLMs find fresh ideas. By combining these approaches, this study reveals new tendencies, points out ongoing difficulties, and offers a comparative analysis of organized thinking in LLMs, so setting the groundwork for further advancements in artificial intelligence driven reasoning models. This report provides a summary of key methodologies presented in eight foundational papers, focusing on their evolution and impact on LLM reasoning.