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Yiding Sun

Yiding Sun has 3 papers in our library, in 2024–2026, most of them on Language models & pretraining, Reasoning, Vision & language; often with Da Pan, Dongxu Zhang, Fan Yang.

3 papers in our library · 2024–2026

OpenAlex · h-index 1 · 3 works · 1 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 7 times in our library · ⭐ Paper Stars ranking: #10250

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

🎓Xi'an Jiaotong UniversityXi'an · China2026 · 1 papercurrent🎓Xidian UniversityXi'an · China2026 · 1 papercurrent

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.

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 3 worksComputer Science · 3 worksArtificial Intelligence · 3 worksLanguage and Linguistics · 3 worksComputer Vision and Pattern Recognition · 1 workInformation Systems · 1 work

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 1 workReasoning · 1 workVision & language · 1 work

 Works by year

2020s

20262 papers20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Chain-of-Thought Compression Should Not Be Blind: V-Skip for Efficient Multimodal Reasoning via Dual-Path Anchoring (2026 · 3 citations)
    Long Chain-of-Thought (CoT) rationales make Multimodal Large Language Models (MLLMs) more accurate, but they also make them slow and memory-hungry.
  2. Search-P1: Path-Centric Reward Shaping for Stable and Efficient Agentic RAG Training (2026 · 2 citations)
    Agentic RAG systems trained with reinforcement learning, such as Search-R1, usually receive a single binary reward for the final answer.
  3. BaichuanSEED: Sharing the Potential of ExtensivE Data Collection and Deduplication by Introducing a Competitive Large Language Model Baseline (2024 · 2 citations)
    Pre-training corpora largely determine what a large language model can do. Yet many developers, including some that release open weights, keep their data collection and mixing recipes private. Some models are also tuned toward particular benchmarks, which hides their real abilities.

 Co-authors here

12 of 15 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 7 times (of the 149,439 papers whose bibliographies we read); 3 citing papers with a page here →.

Updated

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