Papers by Hongchao Gu
SPARD: Self-Paced Curriculum for RL Alignment via Integrating Reward Dynamics and Data Utility (2026.acl-long)
Copied to clipboard
Xuyang Zhi, Peilun Zhou, Chengqiang Lu, Hang Lv, Yiwei Liang, Rongyang Zhang, Yan Gao, null Yiwu, Yao Hu, Hongchao Gu, Defu Lian, Hao Wang, Enhong Chen
| Challenge: | Large Language Models (LLMs) are shifting the focus from single verifiable tasks toward complex, open-ended real-world scenarios. |
| Approach: | They propose a framework that automatically adjusts reward weights and data importance to synchronize learning intent with data utility for optimal performance. |
| Outcome: | The proposed framework improves model capabilities across all domains and scales. |
Learning from Emptiness: De-biasing Listwise Rerankers with Content-Agnostic Probability Calibration (2026.acl-short)
Copied to clipboard
| Challenge: | Existing methods for listwise reranking exhibit intrinsic position bias . existing methods are constrained by an inherent trade-off between efficiency and flexibility . |
| Approach: | They propose a training-free framework that mechanically decouples positional bias from ranking decisions. |
| Outcome: | a training-free framework decouples position bias from ranking decisions . evaluations show it outperforms training-based methods and outperformed expensive methods . |
RAPID: Efficient Retrieval-Augmented Long Text Generation with Writing Planning and Information Discovery (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for knowledge-intensive long texts struggle with issues like hallucinations, topic incoherence, and significant latency. |
| Approach: | They propose a retrieval-augmented long text generation framework with writing P**lanning and I**nformation to address these challenges. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on a freshWiki-2024 dataset. |