SEED-GRPO: Semantic Entropy Enhanced GRPO for Uncertainty-Aware Policy Optimization

• URL: http://arxiv.org/abs/2505.12346v1
• Date: Sun, 18 May 2025 10:20:59 GMT
• Title: SEED-GRPO: Semantic Entropy Enhanced GRPO for Uncertainty-Aware Policy Optimization
• Authors: Minghan Chen, Guikun Chen, Wenguan Wang, Yi Yang,
• Abstract summary: Large language models (LLMs) exhibit varying levels of confidence across input prompts (questions)<n>Semantic entropy measures the diversity of meaning in multiple generated answers given a prompt and uses this to modulate the magnitude of policy updates.
• Score: 57.69385990442078
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Reinforcement Inference: Leveraging Uncertainty for Self-Correcting Language Model Reasoning [0.0]
  Reinforcement Inference uses the model's own uncertainty to selectively invoke a second, more deliberate reasoning attempt.<n>On 12,032 MMLU-Pro questions across 14 subjects, using DeepSeek-v3.2 with deterministic decoding in a zero-shot setting, Reinforcement Inference improves accuracy from 60.72% to 84.03%.
  arXiv  Detail & Related papers  (2026-02-09T11:08:24Z)
• Back to Basics: Revisiting Exploration in Reinforcement Learning for LLM Reasoning via Generative Probabilities [10.235183326885794]
  Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as an indispensable paradigm for enhancing reasoning in Large Language Models (LLMs)<n>We analyze this issue from the perspective of sampling probability dynamics, identifying that the standard objective disproportionately reinforces the highest-likelihood paths.<n>We propose a novel Advantage Re-weighting Mechanism (ARM) designed to equilibrate the confidence levels across all correct responses.
  arXiv  Detail & Related papers  (2026-02-05T04:06:55Z)
• Information-Consistent Language Model Recommendations through Group Relative Policy Optimization [3.969918993270049]
  Large Language Models (LLMs) are increasingly deployed in business-critical domains such as finance, education, healthcare, and customer support.<n>LLMs often exhibit variability when prompts are phrased with minor differences, even when semantically equivalent.<n>We propose a reinforcement learning framework based on Group Relative Policy Optimization (GRPO) to optimize for consistency.
  arXiv  Detail & Related papers  (2025-12-14T21:52:31Z)
• GRPO-Guard: Mitigating Implicit Over-Optimization in Flow Matching via Regulated Clipping [63.33669214116784]
  GRPO-Guard is a simple yet effective enhancement to existing GRPO frameworks.<n>It restores a balanced and step-consistent importance ratio, ensuring that PPO clipping properly constrains harmful updates.<n>It substantially mitigates implicit over-optimization without relying on heavy KL regularization.
  arXiv  Detail & Related papers  (2025-10-25T14:51:17Z)
• ERGO: Entropy-guided Resetting for Generation Optimization in Multi-turn Language Models [9.509429747913519]
  Large Language Models (LLMs) suffer significant performance degradation in multi-turn conversations when information is presented incrementally.<n>We introduce ERGO, which quantifies internal uncertainty via Shannon entropy over next token distributions.<n> ERGO embraces variability in language and modeling, representing and responding to uncertainty.
  arXiv  Detail & Related papers  (2025-10-15T20:33:08Z)
• Conformal Information Pursuit for Interactively Guiding Large Language Models [64.39770942422288]
  This paper explores sequential querying strategies that aim to minimize the expected number of queries.<n>One such strategy is Information Pursuit (IP), a greedy algorithm that at each iteration selects the query that maximizes information gain or equivalently minimizes uncertainty.<n>We propose Conformal Information Pursuit (C-IP), an alternative approach to sequential information gain based on conformal prediction sets.
  arXiv  Detail & Related papers  (2025-07-04T03:55:39Z)
• UProp: Investigating the Uncertainty Propagation of LLMs in Multi-Step Agentic Decision-Making [47.64013151246807]
  Large Language Models (LLMs) are integrated into safety-critical applications involving sequential decision-making.<n>Existing LLM Uncertainty Quantification (UQ) methods are primarily designed for single-turn question-answering formats.<n>We introduce a principled, information-theoretic framework that decomposes LLM sequential decision uncertainty into two parts.
  arXiv  Detail & Related papers  (2025-06-20T18:34:04Z)
• Inv-Entropy: A Fully Probabilistic Framework for Uncertainty Quantification in Language Models [5.6672926445919165]
  Large language models (LLMs) have transformed natural language processing, but their reliable deployment requires effective uncertainty quantification (UQ)<n>Existing UQ methods are often and lack a probabilistic foundation.<n>We propose a fully probabilistic framework based on an inverse model, which quantifies uncertainty by evaluating the diversity of the input space conditioned on a given output through systematic perturbations.
  arXiv  Detail & Related papers  (2025-06-11T13:02:17Z)
• Representation-based Reward Modeling for Efficient Safety Alignment of Large Language Model [84.00480999255628]
  Reinforcement Learning algorithms for safety alignment of Large Language Models (LLMs) encounter the challenge of distribution shift.<n>Current approaches typically address this issue through online sampling from the target policy.<n>We propose a new framework that leverages the model's intrinsic safety judgment capability to extract reward signals.
  arXiv  Detail & Related papers  (2025-03-13T06:40:34Z)
• Assessing Correctness in LLM-Based Code Generation via Uncertainty Estimation [0.0]
  We explore uncertainty estimation as a proxy for correctness in LLM-generated code.<n>We adapt two state-of-the-art techniques from natural language generation to the domain of code generation.<n>Our findings indicate a strong correlation between the uncertainty computed through these techniques and correctness.
  arXiv  Detail & Related papers  (2025-02-17T10:03:01Z)
• Auto-Prompt Generation is Not Robust: Prompt Optimization Driven by Pseudo Gradient [50.15090865963094]
  We introduce PertBench, a comprehensive benchmark dataset that includes a wide range of input perturbations.<n>Our analysis reveals substantial vulnerabilities in existing prompt generation strategies.<n>We propose PGO, a gradient-free prompt generation framework that leverages perturbation types as pseudo-gradient signals.
  arXiv  Detail & Related papers  (2024-12-24T06:05:08Z)
• Improving Uncertainty Quantification in Large Language Models via Semantic Embeddings [11.33157177182775]
  Accurately quantifying uncertainty in large language models (LLMs) is crucial for their reliable deployment. Current state-of-the-art methods for measuring semantic uncertainty in LLMs rely on strict bidirectional entailment criteria. We propose a novel approach that leverages semantic embeddings to achieve smoother and more robust estimation of semantic uncertainty.
  arXiv  Detail & Related papers  (2024-10-30T04:41:46Z)
• Cycles of Thought: Measuring LLM Confidence through Stable Explanations [53.15438489398938]
  Large language models (LLMs) can reach and even surpass human-level accuracy on a variety of benchmarks, but their overconfidence in incorrect responses is still a well-documented failure mode. We propose a framework for measuring an LLM's uncertainty with respect to the distribution of generated explanations for an answer.
  arXiv  Detail & Related papers  (2024-06-05T16:35:30Z)
• Kernel Language Entropy: Fine-grained Uncertainty Quantification for LLMs from Semantic Similarities [79.9629927171974]
  Uncertainty in Large Language Models (LLMs) is crucial for applications where safety and reliability are important. We propose Kernel Language Entropy (KLE), a novel method for uncertainty estimation in white- and black-box LLMs.
  arXiv  Detail & Related papers  (2024-05-30T12:42:05Z)
• Uncertainty in Language Models: Assessment through Rank-Calibration [65.10149293133846]
  Language Models (LMs) have shown promising performance in natural language generation. It is crucial to correctly quantify their uncertainty in responding to given inputs. We develop a novel and practical framework, termed $Rank$-$Calibration$, to assess uncertainty and confidence measures for LMs.
  arXiv  Detail & Related papers  (2024-04-04T02:31:05Z)
• Minimum-Delay Adaptation in Non-Stationary Reinforcement Learning via Online High-Confidence Change-Point Detection [7.685002911021767]
  We introduce an algorithm that efficiently learns policies in non-stationary environments. It analyzes a possibly infinite stream of data and computes, in real-time, high-confidence change-point detection statistics. We show that (i) this algorithm minimizes the delay until unforeseen changes to a context are detected, thereby allowing for rapid responses.
  arXiv  Detail & Related papers  (2021-05-20T01:57:52Z)
• Risk-Sensitive Markov Decision Processes with Combined Metrics of Mean and Variance [3.062772835338966]
  This paper investigates the optimization problem of an infinite stage discrete time Markov decision process (MDP) with a long-run average metric. A performance difference formula is derived and it can quantify the difference of the mean-variance combined metrics of MDPs under any two different policies. A necessary condition of the optimal policy and the optimality of deterministic policies are derived.
  arXiv  Detail & Related papers  (2020-08-09T10:35:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.