Yuxuan Hu
Yuxuan Hu has 4 papers in our library, in 2007–2026, most of them on Word sense disambiguation, Language models & pretraining, Reasoning; often with Ke Wang, Bohan Zhang, Jing Zhang.
OpenAlex · h-index 0 · 3 works · 0 citations OpenAlex, 2026-10-14
📄 4 works in our library · 🔗 Cited 9 times in our library · ⭐ Paper Stars ranking: #9127
Affiliations
Fields, topics and concepts
📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 4 worksArtificial Intelligence · 3 worksArts and Humanities · 3 worksLanguage and Linguistics · 3 worksComputational Theory and Mathematics · 1 workComputer Vision and Pattern Recognition · 1 work
🧭 Topics 5Research subjects and languages the work is about.Language models & pretraining · 1 workEnglish · 1 workWord sense disambiguation · 1 workReasoning · 1 workVision & language · 1 work
Works by year
2020s
2000s
Papers
- HIT-IR-WSD: A WSD System for English Lexical Sample Task (2007 · 12 citations)
The paper describes HIT-IR-WSD, a supervised word sense disambiguation system from the Information Retrieval Lab at Harbin Institute of Technology, created for SemEval 2007's Task 11, which tested disambiguation of English words in a lexical sample setting. - Sparse-RL: Breaking the Memory Wall in LLM Reinforcement Learning via Stable Sparse Rollouts (2026 · 4 citations)
Long reasoning rollouts in LLM reinforcement learning store large KV caches. This memory cost limits batch sizes and GPU utilization, especially since rollout generation takes most of the training time. The authors ask whether training-free KV compression, built for inference, can be used during rollouts. They find… - MathCanvas: Intrinsic Visual Chain-of-Thought for Multimodal Mathematical Reasoning (2026 · 3 citations)
Text-only chain-of-thought is a poor fit for areas such as geometry and function analysis, where people solve problems by sketching and modifying diagrams. - MTP-RL: Acceleration of Reinforcement Learning Rollouts with Policy-Aligned Multi-Token Prediction (2026)
Rollout generation consumes most of the compute in reinforcement-learning fine-tuning of LLMs, especially when models produce long reasoning chains.
Co-authors here
- Ke Wang ×3
- Bohan Zhang ×2
- Jing Zhang ×2
- Aohan Zeng
- Aojun Zhou
- Houxing Ren
- Jie Tang
- Lei Liang
- Mengshu Sun
- Rongyao Fang
- Sijia Luo
- Ting Liu
Venues
- Annual Meeting of the Association for Computational Linguistics (ACL) ×2: ACL 2026 ×2
- Findings of the Association for Computational Linguistics: Findings of the Association for Computational Linguistics 2026
- International Workshop on Semantic Evaluations: International Workshop on Semantic Evaluations 2007
Cited by
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