SEED-GRPO: Semantic Entropy Enhanced GRPO for Uncertainty-Aware Policy
  Optimization
Large language models (LLMs) exhibit varying levels of confidence across
input prompts (questions): some lead to consistent, semantically similar
answers, while others yield diverse or contradictory outputs. This variation
reflects LLM's uncertainty about the input prompt, a signal of how confidently
the model understands a given problem. However, vanilla Group Relative Policy
Optimization (GRPO) treats all prompts equally during policy updates, ignoring
this important information about the model's knowledge boundaries. To address
this limitation, we propose SEED-GRPO (Semantic Entropy EnhanceD GRPO), which
explicitly measures LLMs' uncertainty of the input prompts semantic entropy.
Semantic entropy measures the diversity of meaning in multiple generated
answers given a prompt and uses this to modulate the magnitude of policy
updates. This uncertainty-aware training mechanism enables dynamic adjustment
of policy update magnitudes based on question uncertainty. It allows more
conservative updates on high-uncertainty questions while maintaining the
original learning signal on confident ones. Experimental results on five
mathematical reasoning benchmarks (AIME24 56.7, AMC 68.7, MATH 83.4, Minerva
34.2, and OlympiadBench 48.0) demonstrate that SEED-GRPO achieves new
state-of-the-art performance in average accuracy, validating the effectiveness
of uncertainty-aware policy optimization.
682be9d44182ec69eba2a1f1
Minghan Chen
claimed_verified
2025-05-20T07:20:47.411Z
6253fb19d3e43081e102af4f
https://cdn-avatars.huggingface.co/v1/production/uploads/6253fb19d3e43081e102af4f/0M0Ksd8rzm_g4k-hl0xP6.jpeg
MC
user
Dreamer312
682be9d44182ec69eba2a1f2
Guikun Chen
682be9d44182ec69eba2a1f3
Wenguan Wang
682be9d44182ec69eba2a1f4
Yi Yang
SEED-GRPO: Semantic Entropy Enhanced GRPO for Uncertainty-Aware Policy
  Optimization
Large language models (LLMs) exhibit varying levels of confidence across
input prompts (questions): some lead to consistent, semantically similar
answers, while others yield diverse or contradictory outputs. This variation
reflects LLM's uncertainty about the input prompt, a signal of how confidently
the model understands a given problem. However, vanilla Group Relative Policy
Optimization (GRPO) treats all prompts equally during policy updates, ignoring
this important information about the model's knowledge boundaries. To address
this limitation, we propose SEED-GRPO (Semantic Entropy EnhanceD GRPO), which
explicitly measures LLMs' uncertainty of the input prompts semantic entropy.
Semantic entropy measures the diversity of meaning in multiple generated
answers given a prompt and uses this to modulate the magnitude of policy
updates. This uncertainty-aware training mechanism enables dynamic adjustment
of policy update magnitudes based on question uncertainty. It allows more
conservative updates on high-uncertainty questions while maintaining the
original learning signal on confident ones. Experimental results on five
mathematical reasoning benchmarks (AIME24 56.7, AMC 68.7, MATH 83.4, Minerva
34.2, and OlympiadBench 48.0) demonstrate that SEED-GRPO achieves new
state-of-the-art performance in average accuracy, validating the effectiveness
of uncertainty-aware policy optimization.
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.
Large language models (LLMs) Group Relative Policy Optimization (GRPO) Semantic Entropy EnhanceD GRPO (SEED-GRPO) semantic entropy uncertainty-aware training