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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.

4 papers in our library · 2007–2026

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

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

 Works by year

2020s

2000s

 Papers

The most cited first (OpenAlex and our library).

  1. 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.
  2. 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…
  3. 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.
  4. 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

12 of 20 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 9 times (of the 149,439 papers whose bibliographies we read); 2 citing papers with a page here →.

Updated

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