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Xin Guo

Xin Guo has 3 papers in our library, in 2023–2024, most of them on Language models & pretraining, Reasoning; often with Boyang Hong, Junzhe Wang, Wei He.

3 papers in our library · 2023–2024

OpenAlex · ORCID · h-index 8 · 21 works · 386 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 422 times in our library · ⭐ Paper Stars ranking: #2234

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

🎓Chengdu College of University of Electronic Science And Technology of ChinaChengdu · China2025 · 1 papercurrent🎓Huazhong Agricultural UniversityWuhan · China2025 · 1 papercurrent🎓University of Electronic Science and Technology of ChinaChengdu · China2012–2020 · 10 papers🎓University of GlasgowGlasgow · United Kingdom2021–2024 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 6 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 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 3 worksComputer Science · 3 worksArts and Humanities · 2 worksLanguage and Linguistics · 2 works

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

💡 Concepts 1Key ideas and methods explained on our concept pages.Large language models · 1 work

 Works by year

2020s

20242 papers20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. The Rise and Potential of Large Language Model Based Agents: A Survey (2023 · 1,272 citations)
    This 2023 survey from the Fudan NLP Group maps the young field of agents built on large language models. The authors argue that earlier agent research mostly tuned algorithms for narrow skills, such as symbolic reasoning or board games like Go and Chess. What the field lacked was a general base model that agents…
  2. Training Large Language Models for Reasoning through Reverse Curriculum Reinforcement Learning (2024 · 36 citations)
    Applying reinforcement learning to multi-step reasoning in LLMs is difficult. Outcome supervision only scores the final answer, so the rewards are sparse and errors cannot be localized. Process supervision gives a reward for every step, but it depends on costly expert annotation and a learned reward model. The…
  3. AgentGym: Evolving Large Language Model-based Agents across Diverse Environments (2024 · 33 citations)
    Today's LLM-based agents are trained in one of two ways. Some copy expert demonstrations, which is costly and limits exploration. Others improve through feedback inside a single environment, which yields narrow specialists. This paper asks whether one agent can first learn by imitation and then keep improving across…

 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 422 times (of the 149,439 papers whose bibliographies we read); 147 citing papers with a page here →.

Updated

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