SEED-GRPO: Semantic Entropy Enhanced GRPO for Uncertainty-Aware Policy Optimization
Minghan Chen, Guikun Chen, Wenguan Wang, Yi Yang
2025-05-20
Summary
This paper talks about SEED-GRPO, a new way to train AI models so they can handle uncertainty better when solving math problems, making their answers more reliable.
What's the problem?
The problem is that AI models often struggle when they're not sure about the information in a question, which can make their answers less accurate, especially in tricky math problems.
What's the solution?
To solve this, the researchers improved an existing training method by making the AI pay more attention to how uncertain it feels about each question. This helps the model adjust its learning and decision-making process, so it can give better answers even when things are unclear.
Why it matters?
This matters because it helps AI become more trustworthy and accurate in situations where the information isn't perfect, which is important for using AI in real-world problem solving, especially in subjects like math.
Abstract
SEED-GRPO enhances Group Relative Policy Optimization by adjusting policy updates based on the uncertainty of input prompts, achieving state-of-the-art performance in mathematical reasoning benchmarks.