Unveiling Implicit Advantage Symmetry: Why GRPO Struggles with Exploration and Difficulty Adaptation
cs.LG, cs.AI
Submitted: 2026-02-05
Updated: 2026-09-27
Code: https://github.com/HKU-HealthAI/A-GRAE
Terminology
Sources
- GPT-4 Technical Report
- Online Difficulty Filtering for Reasoning Oriented Reinforcement Learning
- XRPO: Pushing the limits of GRPO with Targeted Exploration and Exploitation
- HuatuoGPT-Vision, Towards Injecting Medical Visual Knowledge into Multimodal LLMs at Scale
- Evaluating Large Language Models Trained on Code
- Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models
- Reasoning with Exploration: An Entropy Perspective
- Boosting the Generalization and Reasoning of Vision Language Models with Curriculum Reinforcement Learning
- Decomposing the Entropy-Performance Exchange: The Missing Keys to Unlocking Effective Reinforcement Learning
- Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRs
- EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO
- DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
- Rethinking Entropy Interventions in RLVR: An Entropy Change Perspective
- PathVQA: 30000+ Questions for Medical Visual Question Answering
- Measuring Mathematical Problem Solving With the MATH Dataset
- A Sober Look at Progress in Language Model Reasoning: Pitfalls and Paths to Reproducibility
- REINFORCE++: Stabilizing Critic-Free Policy Optimization with Global Advantage Normalization
- Semantic-Space Exploration and Exploitation in RLVR for LLM Reasoning
- OpenAI o1 System Card
- PuzzleCraft: Exploration-Aware Curriculum Learning for Puzzle-Based RLVR in VLMs
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