Reasoning with Exploration: An Entropy Perspective
Daixuan Cheng, Shaohan Huang, Xuekai Zhu, Bo Dai, Wayne Xin Zhao, Zhenliang Zhang, Furu Wei
2025-06-18
Summary
This paper talks about a new way to help language models think better by encouraging them to explore more ideas when solving difficult problems, using a concept called entropy in reinforcement learning.
What's the problem?
The problem is that language models sometimes get stuck giving the same kinds of answers and don’t explore different possibilities, which limits their ability to solve complex reasoning tasks well.
What's the solution?
The researchers added an entropy-based term to the reinforcement learning process, which rewards the model for exploring different ideas and thinking in more diverse ways. This helps the model find better solutions by considering more options.
Why it matters?
This matters because encouraging AI to explore more ideas makes it better at solving complicated problems and improves its overall reasoning skills, making it more useful in real-world situations.
Abstract
Introducing an entropy-based term to the advantage function in reinforcement learning enhances exploratory reasoning in language models, leading to improved performance on complex reasoning tasks.