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Ling Feng

Ling Feng has 3 papers in our library, in 2025–2026, most of them on Language models & pretraining, Reasoning; often with Jiajie Zhang, Juanzi Li, Lei Hou.

3 papers in our library · 2025–2026

OpenAlex · ORCID · h-index 2 · 13 works · 14 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 56 times in our library · ⭐ Paper Stars ranking: #8641

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

🎓Tsinghua UniversityBeijing · China2025 · 1 papercurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 5 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.

📚 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 2Research subjects and languages the work is about.Reasoning · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20261 paper20252 papers

 Papers

The most cited first (OpenAlex and our library).

  1. AdaptThink: Reasoning Models Can Learn When to Think (2025 · 44 citations)
    Large reasoning models such as DeepSeek-R1 produce a long chain of thought before answering, even for easy problems.
  2. LongReward: Improving Long-context Large Language Models with AI Feedback (2025 · 21 citations)
    Long-context LLMs are usually fine-tuned on question-answer data that other LLMs generate automatically. That data often contains logical slips, invented facts or incomplete answers, and these flaws carry over into the SFT models. Reinforcement learning could correct such behaviour, but there has been no dependable…
  3. Chaining the Evidence: Robust Reinforcement Learning for Deep Search Agents with Citation-Aware Rubric Rewards (2026)
    Deep search agents built on LLMs are usually trained with reinforcement learning using a binary reward: the final answer is either right or wrong.

 Co-authors here

9 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 56 times (of the 149,439 papers whose bibliographies we read); 26 citing papers with a page here →.

Updated

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