A Survey of Slow Thinking-based Reasoning LLMs using Reinforced Learning and Inference-time Scaling Law
Qianjun Pan, Wenkai Ji, Yuyang Ding, Junsong Li, Shilian Chen, Junyi Wang, Jie Zhou, Qin Chen, Min Zhang, Yulan Wu, Liang He·May 05, 2025
Summary
The survey examines Large Language Models (LLMs) for "slow thinking," categorizing methods into test-time scaling, reinforced learning, and slow-thinking frameworks. It aims to merge human-like deep thinking with scalable efficiency for reasoning, synthesizing insights from over 100 studies. Key areas include integrating supervision, using reinforcement learning for tasks like medical reasoning, and categorizing reward models. Recent research focuses on speculative decoding, scalable oversight, reinforcement learning with large language models, visual reasoning, transfer learning in network biology, open foundation language models, and solving math word problems with feedback. Contributions also address safety in deep learning, multimodal reasoning, and improving model stability and adaptability in computer vision tasks.
Introduction
Background
Definition of "slow thinking" in the context of AI
Importance of "slow thinking" in human-like reasoning and decision-making
Objective
To explore and categorize methods in LLMs that facilitate "slow thinking"
To merge human-like deep thinking with scalable efficiency for reasoning
To synthesize insights from over 100 studies on LLMs and "slow thinking"
Method
Data Collection
Overview of studies and research papers on LLMs and "slow thinking"
Criteria for selecting relevant studies and papers
Data Preprocessing
Categorization of studies into test-time scaling, reinforced learning, and slow-thinking frameworks
Extraction of key findings and methodologies from each category
Test-Time Scaling
Techniques and Applications
Explanation of test-time scaling methods
Case studies demonstrating the use of test-time scaling in LLMs for "slow thinking"
Challenges and Limitations
Discussion of challenges faced in implementing test-time scaling
Analysis of limitations in enhancing "slow thinking" through this method
Reinforced Learning
Reinforcement Learning Frameworks
Overview of reinforcement learning in LLMs
Different reinforcement learning models and their applications
Case Studies
Detailed analysis of reinforcement learning in tasks like medical reasoning
Future Directions
Potential advancements and challenges in reinforcement learning for "slow thinking"
Slow-Thinking Frameworks
Integration of Supervision
Role of supervision in enhancing "slow thinking" in LLMs
Techniques for integrating supervision effectively
Reward Models
Categorization and analysis of reward models in LLMs
Impact of different reward models on "slow thinking" capabilities
Recent Research Focus
Speculative Decoding
Explanation of speculative decoding in LLMs
Applications and benefits of speculative decoding for "slow thinking"
Scalable Oversight
Overview of scalable oversight techniques
How scalable oversight improves "slow thinking" in LLMs
Reinforcement Learning with Large Language Models
Recent advancements in reinforcement learning with LLMs
Case studies and applications in various domains
Visual Reasoning
Integration of visual information in LLMs for reasoning
Applications and challenges in visual reasoning with LLMs
Transfer Learning in Network Biology
Use of transfer learning in network biology with LLMs
Advantages and limitations of transfer learning in this context
Open Foundation Language Models
Overview of open foundation language models
Potential for "slow thinking" in open foundation models
Solving Math Word Problems with Feedback
Techniques for solving math word problems using LLMs
Role of feedback in enhancing "slow thinking" in mathematical reasoning
Contributions to Safety and Adaptability
Safety in Deep Learning
Importance of safety in deep learning models
Contributions of LLMs to improving safety in AI systems
Multimodal Reasoning
Integration of multiple modalities in reasoning with LLMs
Advancements in multimodal reasoning and their impact
Improving Model Stability and Adaptability
Techniques for enhancing model stability and adaptability in computer vision tasks
Case studies demonstrating improvements in stability and adaptability
Conclusion
Summary of Findings
Key insights and advancements in LLMs for "slow thinking"
Future Research Directions
Open questions and areas for further exploration
Implications for AI and Human-like Reasoning
Potential implications of LLMs for AI and human-like reasoning in various fields
Basic info
papers
artificial intelligence
Advanced features
Insights
How does the survey categorize methods for integrating slow thinking in Large Language Models?
What innovative approaches are discussed for enhancing reasoning and efficiency in Large Language Models?
What are the primary objectives of the survey on Large Language Models for slow thinking?
What are the identified limitations and challenges in applying slow thinking frameworks to Large Language Models?