DGRO: Enhancing LLM Reasoning via Exploration-Exploitation Control and Reward Variance Management
Xuerui Su, Liya Guo, Yue Wang, Yi Zhu, Zhiming Ma, Zun Wang, Yuting Liu·May 19, 2025
Summary
DGRO enhances large language models' reasoning, achieving 96.9% accuracy on Logic datasets. It outperforms base models on benchmarks like K&K and Math, balancing exploration and exploitation. Recent deep reinforcement learning focuses on integrating human preferences and logic reasoning. DGRO validates its effectiveness, surpassing base models in reasoning tasks.
Introduction
Background
Overview of large language models
Importance of reasoning in language models
Previous approaches to improving reasoning capabilities
Objective
The goal of DGRO in enhancing reasoning abilities
Key achievements of DGRO on Logic datasets and benchmarks
Method
Data Collection
Types of data used for training and evaluation
Sources of data for Logic datasets and benchmarks
Data Preprocessing
Techniques applied to data for improving model performance
Handling of specific challenges in Logic datasets and benchmarks
Integration of Human Preferences and Logic Reasoning
Strategies for incorporating human preferences into the model
Methods for enhancing logical reasoning within the model architecture
Deep Reinforcement Learning Approach
Overview of the reinforcement learning framework used by DGRO
How exploration and exploitation are balanced in the learning process
Results
Performance on Logic Datasets
Detailed accuracy metrics on Logic datasets
Comparison with base models
Benchmark Performance
Results on benchmarks like K&K and Math
DGRO's superiority over base models
Effectiveness in Reasoning Tasks
Specific reasoning tasks DGRO excels in
Analysis of how DGRO surpasses base models in these tasks
Conclusion
Summary of DGRO's Contributions
Recap of DGRO's improvements in reasoning capabilities
Future Directions
Potential areas for further research and development
Integration of DGRO with other AI techniques
Basic info
papers
machine learning
artificial intelligence
Advanced features
Insights
What are the core components of DGRO that contribute to its effectiveness in reasoning tasks?
In what ways does DGRO outperform base models on benchmarks like K&K and Math?
How does DGRO enhance reasoning capabilities in large language models?
How does DGRO balance exploration and exploitation in reasoning tasks?