# Advancing Reasoning in Large Language Models: Promising Methods and Approaches

arXiv: https://arxiv.org/abs/2502.03671 · HTML: https://mlsanity.com/paper/2502.03671/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 43 of its 49 references that are in the map.
- First posted on arXiv 5 Feb 2025.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 30 of 43)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Measuring Mathematical Problem Solving With the MATH Dataset](https://mlsanity.com/paper/2103.03874/) (2021)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge](https://mlsanity.com/paper/1803.05457/) (2018)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://mlsanity.com/paper/1809.09600/) (2018)
- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://mlsanity.com/paper/2206.04615/) (2022)
- [A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://mlsanity.com/paper/2311.05232/) (2023)
- [STaR: Bootstrapping Reasoning With Reasoning](https://mlsanity.com/paper/2203.14465/) (2022)
- [Adversarial NLI: A New Benchmark for Natural Language Understanding](https://mlsanity.com/paper/1910.14599/) (2019)
- [Retrieval Augmentation Reduces Hallucination in Conversation](https://mlsanity.com/paper/2104.07567/) (2021)
- [PAL: Program-aided Language Models](https://mlsanity.com/paper/2211.10435/) (2022)
- [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://mlsanity.com/paper/2002.00388/) (2020)
- [Training Language Models to Self-Correct via Reinforcement Learning](https://mlsanity.com/paper/2409.12917/) (2024)
- [Augmented Language Models: a Survey](https://mlsanity.com/paper/2302.07842/) (2023)
- [A simple neural network module for relational reasoning](https://mlsanity.com/paper/1706.01427/) (2017)

## Built on by (most cited first, 30 of 43)

- [Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models](https://mlsanity.com/paper/2503.09567/) (2025)
- [Agentic Reasoning and Tool Integration for LLMs via Reinforcement Learning](https://mlsanity.com/paper/2505.01441/) (2025)
- [StepSearch: Igniting LLMs Search Ability via Step-Wise Proximal Policy Optimization](https://mlsanity.com/paper/2505.15107/) (2025)
- [Towards Robust Legal Reasoning: Harnessing Logical LLMs in Law](https://mlsanity.com/paper/2502.17638/) (2025)
- [O2-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering](https://mlsanity.com/paper/2505.16582/) (2025)
- [A Survey on Mathematical Reasoning and Optimization with Large Language Models](https://mlsanity.com/paper/2503.17726/) (2025)
- [Controllable Logical Hypothesis Generation for Abductive Reasoning in Knowledge Graphs](https://mlsanity.com/paper/2505.20948/) (2025)
- [Safety Evaluation of DeepSeek Models in Chinese Contexts](https://mlsanity.com/paper/2502.11137/) (2025)
- [EPO: Explicit Policy Optimization for Strategic Reasoning in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2502.12486/) (2025)
- [ToolDreamer: Instilling LLM Reasoning Into Tool Retrievers](https://mlsanity.com/paper/2510.19791/) (2025)
- [Explainable LLM Unlearning Through Reasoning](https://mlsanity.com/paper/2603.09980/) (2026)
- [DTS: Enhancing Large Reasoning Models via Decoding Tree Sketching](https://mlsanity.com/paper/2511.00640/) (2025)
- [Experience-Evolving Multi-Turn Tool-Use Agent with Hybrid Episodic-Procedural Memory](https://mlsanity.com/paper/2512.07287/) (2025)
- [Cognitive-Mental-LLM: Evaluating Reasoning in Large Language Models for Mental Health Prediction via Online Text](https://mlsanity.com/paper/2503.10095/) (2025)
- [Reasoning Towards Fairness: Mitigating Bias in Language Models through Reasoning-Guided Fine-Tuning](https://mlsanity.com/paper/2504.05632/) (2025)
- [CoTox: Chain-of-Thought-Based Molecular Toxicity Reasoning and Prediction](https://mlsanity.com/paper/2508.03159/) (2025)
- [Safety Evaluation and Enhancement of DeepSeek Models in Chinese Contexts](https://mlsanity.com/paper/2503.16529/) (2025)
- [Reasoning Can Hurt the Inductive Abilities of Large Language Models](https://mlsanity.com/paper/2505.24225/) (2025)
- [Less is More Tokens: Efficient Math Reasoning via Difficulty-Aware Chain-of-Thought Distillation](https://mlsanity.com/paper/2509.05226/) (2025)
- [AdvChain: Adversarial Chain-of-Thought Tuning for Robust Safety Alignment of Large Reasoning Models](https://mlsanity.com/paper/2509.24269/) (2025)
- [AURA: An Agent Autonomy Risk Assessment Framework](https://mlsanity.com/paper/2510.15739/) (2025)
- [Unifying Stable Optimization and Reference Regularization in RLHF](https://mlsanity.com/paper/2602.11523/) (2026)
- [OPPO: Bayesian Value Recursion for Token-Level Credit Assignment in LLM Reasoning](https://mlsanity.com/paper/2605.21851/) (2026)
- [Learning from Your Own Mistakes: Constructing Learnable Micro-Reflective Trajectories for Self-Distillation](https://mlsanity.com/paper/2606.18844/) (2026)
- [Evaluating Multi-Hop Reasoning in Large Language Models: A Chemistry-Centric Case Study](https://mlsanity.com/paper/2504.16414/) (2025)
- [LM-mixup: Text Data Augmentation via Language Model based Mixup](https://mlsanity.com/paper/2510.20449/) (2025)
- [Rethinking Facial Expression Recognition in the Era of Multimodal Large Language Models: Benchmark, Datasets, and Beyond](https://mlsanity.com/paper/2511.00389/) (2025)
- [GCPO: When Contrast Fails, Go Gold](https://mlsanity.com/paper/2510.07790/) (2025)
- [Two-way Evidence self-Alignment based Dual-Gated Reasoning Enhancement](https://mlsanity.com/paper/2505.16806/) (2025)
- [JURY-RL: Votes Propose, Proofs Dispose for Label-Free RLVR](https://mlsanity.com/paper/2604.25419/) (2026)

## Abstract (arXiv)

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.