Papers by Wenge Que
Graph-GRPO: Stabilizing Multi-Agent Topology Learning via Group Relative Policy Optimization (2026.findings-acl)
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Yueyang Cang, Xiaoteng Zhang, Erlu Zhao, Zehua Ji, Yuhang Liu, Yuchen He, Zhiyuan Ning, Chen Yijun, Wenge Que, Li Shi
| Challenge: | Recent approaches to optimize communication topology rely on single-sample policy gradients with absolute rewards. |
| Approach: | They propose a topology optimization framework that integrates Group Relative Policy Optimization. |
| Outcome: | The proposed topology optimization framework outperforms state-of-the-art methods on reasoning and code generation benchmarks. |
PIC: Unlocking Long-Form Text Generation Capabilities of Large Language Models via Position ID Compression (2025.acl-long)
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| Challenge: | Long-context understanding is crucial for large language models (LLMs) however, the ability to “output-long” is underexplored. |
| Approach: | They propose a position ID compression approach to unlock the long-form text generation potential of large language models (LLMs). |
| Outcome: | The proposed approach can extend LLMs' generation length by 1.5 times without compromising generation quality. |