Advancing Reasoning in Large Language Models: Promising Methods and Approaches
Avinash Patil, Aryan Jadon
TL;DR
This survey analyzes the problem of enhancing reasoning in large language models, detailing prompting techniques (CoT, Self-Consistency, ToT, PAL), architectural innovations (RAG, neuro-symbolic, memory, graphs, tools), and learning-based approaches (supervised fine-tuning, RLHF, SSL/contrastive). It highlights evaluation frameworks and benchmarks, and discusses persistent challenges such as hallucinations, robustness, and cross-domain generalization. The work synthesizes a roadmap of promising directions—grounding, hybrid reasoning, and verifiable checks—to advance reasoning-augmented LLMs for reliable, real-world deployment. The findings underscore the potential of combining structured reasoning with neural models to achieve human-like reliability in complex tasks across domains.
Abstract
Large Language Models (LLMs) have succeeded remarkably in various natural language processing (NLP) tasks, yet their reasoning capabilities remain a fundamental challenge. While LLMs exhibit impressive fluency and factual recall, their ability to perform complex reasoning-spanning logical deduction, mathematical problem-solving, commonsense inference, and multi-step reasoning-often falls short of human expectations. This survey provides a comprehensive review of emerging techniques enhancing reasoning in LLMs. We categorize existing methods into key approaches, including prompting strategies (e.g., Chain-of-Thought reasoning, Self-Consistency, and Tree-of-Thought reasoning), architectural innovations (e.g., retrieval-augmented models, modular reasoning networks, and neuro-symbolic integration), and learning paradigms (e.g., fine-tuning with reasoning-specific datasets, reinforcement learning, and self-supervised reasoning objectives). Additionally, we explore evaluation frameworks used to assess reasoning in LLMs and highlight open challenges, such as hallucinations, robustness, and reasoning generalization across diverse tasks. By synthesizing recent advancements, this survey aims to provide insights into promising directions for future research and practical applications of reasoning-augmented LLMs.