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Chengquan Jiang

Chengquan Jiang has 2 papers in our library, in 2024–2025, most of them on Language models & pretraining, Reasoning, GPU & parallel computing; often with Ge Zhang, Haibin Lin, Jiazhan Feng.

2 papers in our library · 2024–2025

OpenAlex · ORCID · h-index 3 · 12 works · 80 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 234 times in our library · ⭐ Paper Stars ranking: #7021

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

🏢ByteDanceBeijing · China2020–2024 · 2 papersEmployment since 2020

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.

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksArtificial Intelligence · 1 workArts and Humanities · 1 workLanguage and Linguistics · 1 workComputer Networks and Communications · 1 workHardware and Architecture · 1 work

🧭 Topics 3Research subjects and languages the work is about.GPU & parallel computing · 1 workLanguage models & pretraining · 1 workReasoning · 1 work

 Works by year

2020s

20251 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. ReTool: Reinforcement Learning for Strategic Tool Use in LLMs (2025 · 88 citations)
    RL-trained reasoning LLMs handle verbal reasoning well but often make mistakes on exact arithmetic, equation solving and symbolic manipulation. A code interpreter can do these steps reliably. ReTool, from ByteDance Seed, trains a model to decide on its own when and how to call such an interpreter while it reasons.
  2. FLUX: Fast Software-based Communication Overlap On GPUs Through Kernel Fusion (2024 · 67 citations)
    Tensor parallelism splits individual layers of large models across GPUs. This lowers latency, but it adds AllGather and ReduceScatter traffic that can take a large share of runtime. Earlier overlap schemes, such as TransformerEngine, cut a GEMM and its collective into a few chunks, one per device, and schedule them…

 Co-authors here

10 co-authors with a page, the most papers together first.

 Cited by

Papers of our library cite their works 234 times (of the 149,439 papers whose bibliographies we read); 71 citing papers with a page here →.

Updated

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