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.
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
Affiliations
Fields, topics and concepts
📚 Shelves 8The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 4 worksLanguage and Linguistics · 3 worksComputer Science · 4 worksArts and Humanities · 3 worksMathematics · 1 workComputational Theory and Mathematics · 1 workComputer Vision and Pattern Recognition · 1 workSoftware · 1 work
🧭 Topics 2Research subjects and languages the work is about.Reasoning · 2 worksLanguage models & pretraining · 2 works
💡 Concepts 2Key ideas and methods explained on our concept pages.Large language models · 1 workMachine learning · 1 work
Works by year
2020s
Papers
- 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. - 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. - 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… - 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
- Bohan Zhang ×2
- Jing Zhang ×2
- Shirong Ma ×2
- Sijia Luo ×2
- Chengda Lu
- Daya Guo
- DeepSeek-AI
- Dejian Yang
- Haowei Zhang
- Jie Tang
- Jifan Yu
- Junxiao Song
Venues
- Annual Meeting of the Association for Computational Linguistics (ACL): ACL 2026
- Nature: Nature 2025
- Conference on Neural Information Processing Systems (NeurIPS): NeurIPS 2024
Cited by
Updated
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