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Weixun Wang

Weixun Wang has 5 papers in our library, in 2024–2026, most of them on Language models & pretraining, Evaluation & benchmarks, Reasoning; often with Jiaheng Liu, Shilong Li, Wenbo Su.

5 papers in our library · 2024–2026

OpenAlex · ORCID · h-index 22 · 101 works · 2,737 citations OpenAlex, 2026-10-14

📄 5 works in our library · 🔗 Cited 246 times in our library · 5 more in our library without a page yet · ⭐ Paper Stars ranking: #5637

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. 1 more institution on single bylines are not listed.

 Fields, topics and concepts

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

 Works by year

2020s

 Papers

The most cited first (OpenAlex and our library).

  1. OpenRLHF: An Easy-to-use, Scalable and High-performance RLHF Framework (2024 · 140 citations)
    Training LLMs with RLHF and RLVR, especially with PPO, is dominated by text generation. The authors say this phase can take more than 90% of runtime when models produce long chain-of-thought outputs. Existing tools also force a trade-off. TRL, DeepSpeed-Chat and ColossalChat are approachable but weak at inference…
  2. Chinese SimpleQA: A Chinese Factuality Evaluation for Large Language Models (2024 · 34 citations)
    Hallucination remains a major weakness of large language models. Factuality is hard to measure when models produce long answers that mix many claims. OpenAI's SimpleQA addressed this with short fact-seeking questions, but it covers only English. Chinese SimpleQA is a Chinese counterpart: 3,000 short questions, each…
  3. Can Large Language Models Detect Errors in Long Chain-of-Thought Reasoning? (2025 · 30 citations)
    Reasoning models in the o1 style produce very long chains of thought (CoT). Until this paper, there was no systematic way to check how good those chains are, or whether other models can find the mistakes in them. The authors built DeltaBench to address both gaps. It contains 1,236 problems from Math, Programming, PCB…
  4. ShopSimulator: Evaluating and Exploring RL-Driven LLM Agent for Shopping Assistants (2026 · 1 citation)
    LLM-based shopping assistants must clarify vague requests through dialogue, use a shopper's profile and history, and pick the right item among near-identical products.
  5. USB: A COMPREHENSIVE AND UNIFIED SAFETY EVALUATION BENCHMARK FOR MULTIMODAL LARGE LANGUAGE MODELS (2026 · 1 citation)
    Existing safety benchmarks for multimodal large language models (MLLMs) cover few risk types and contain few samples.

All works

10 works of theirs in our library, the most cited first; 5 with a page here, the others link their original until we summarise them.

  1. Multi-Agent Game Abstraction via Graph Attention Neural Network ↗Proceedings of the AAAI Conference on Artificial Intelligence · no summary yetYong Liu et al.2020 · 247 citations
  2. OpenRLHF: An Easy-to-use, Scalable and High-performance RLHF FrameworkarXiv:2405.11143Jian Hu, Xibin Wu, Wei Shen, et al.2024 · 140 citations
  3. Chinese SimpleQA: A Chinese Factuality Evaluation for Large Language ModelsarXiv:2411.07140Yancheng He, Shilong Li, Jiaheng Liu, et al.2024 · 34 citations
  4. Part I: Tricks or Traps? A Deep Dive into RL for LLM Reasoning ↗arXiv:2508.08221 · no summary yetZihe Liu et al.2025 · 15 citations
  5. The N+ Implementation Details of RLHF with PPO: A Case Study on TL;DR Summarization ↗arXiv:2403.17031 · no summary yetShengyi Huang et al.2024 · 14 citations
  6. GEM: A Gym for Agentic LLMs ↗arXiv (Cornell University) · no summary yetLiu, Zichen et al.2025 · 6 citations

 Co-authors here

12 of 28 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 246 times (of the 149,439 papers whose bibliographies we read); 52 citing papers with a page here →.

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