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Yi Su

Yi Su has 3 papers in our library, in 2024–2026, most of them on Language models & pretraining, Efficient models & compression; often with Juntao Li, Min Zhang, Avi Singh.

3 papers in our library · 2024–2026

OpenAlex · ORCID · h-index 5 · 14 works · 64 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 172 times in our library · ⭐ Paper Stars ranking: #5771

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

🎓Huazhong University of Science and TechnologyWuhan · China2017–2025 · 8 paperscurrent🎓Soochow UniversitySuzhou · China2025 · 1 papercurrent🔬Wuhan National Laboratory for OptoelectronicsWuhan · China2018–2020 · 3 papers

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 5The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 3 worksLanguage and Linguistics · 3 worksArts and Humanities · 3 worksComputer Science · 3 worksMathematics · 1 work

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 1 workEfficient models & compression · 1 work

 Works by year

2020s

20261 paper20251 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Training Language Models to Self-Correct via Reinforcement Learning (2024 · 159 citations)
    Large language models rarely fix their own mistakes when they get no outside feedback, a setting called intrinsic self-correction.
  2. OPT-Tree: Speculative Decoding with Adaptive Draft Tree Structure (2025 · 32 citations)
    Speculative decoding speeds up LLM inference without changing outputs: a cheap draft model proposes several tokens, and the large target model checks them all in one parallel pass.
  3. Crossing the Reward Bridge: Expanding Reinforcement Learning with Verifiable Rewards Across Diverse Domains (2026)
    RLVR works well for math and code, where a simple rule can check whether an answer is right. Such checkers fail on free-form answers, and the authors show that rule-based rewards can even get worse as more mixed-domain training data is added. The paper replaces the rule with a generative verifier. An LLM compares the…

 Co-authors here

12 of 17 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 172 times (of the 149,439 papers whose bibliographies we read); 37 citing papers with a page here →.

Updated

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