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Xiaokang Zhang

Xiaokang Zhang has 4 papers in our library, in 2024–2026, most of them on Language models & pretraining, Reasoning; often with Bohan Zhang, Jing Zhang, Shirong Ma.

4 papers in our library · 2024–2026

OpenAlex · ORCID · h-index 28 · 119 works · 3,062 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 3,682 times in our library · ⭐ Paper Stars ranking: #687

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. 26 more institutions 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. DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning (2025 · 4,728 citations)
    The paper asks whether a large language model can learn to reason well without human-written reasoning traces, which are costly, biased, and cap the model at human-level demonstrations.
  2. DeepSeekMath-V2: Towards Self-Verifiable Mathematical Reasoning (2025 · 33 citations)
    Reinforcement learning that only checks final answers has let LLMs master answer-based contests like AIME and HMMT.
  3. SpreadsheetBench: Towards Challenging Real World Spreadsheet Manipulation (2024 · 21 citations)
    SpreadsheetBench tackles a weakness in how LLM-based spreadsheet assistants are evaluated. Earlier benchmarks rely on synthetic or crowdsourced instructions, simple single-table sheets, and a single check per task. The authors collected solved questions from four Excel help forums (ExcelForum, Chandoo, MrExcel…
  4. 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…

 Co-authors here

12 of 27 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 3,682 times (of the 149,439 papers whose bibliographies we read); 812 citing papers with a page here →.

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