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Kechi Zhang

Kechi Zhang has 3 papers in our library, in 2024–2026, most of them on Code & programming languages, Language models & pretraining; often with Zhi Jin, Ge Li, Yihong Dong.

3 papers in our library · 2024–2026

OpenAlex · ORCID · Wikidata · h-index 9 · 34 works · 345 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 123 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #5908

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

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

 Works by year

2020s

 Papers

The most cited first (OpenAlex and our library).

  1. CodeAgent: Enhancing Code Generation with Tool-Integrated Agent Systems for Real-World Repo-level Coding Challenges (2024 · 150 citations)
    Most LLM code generation research targets short, self-contained snippets. Real projects differ: most functions depend on other code in the same repository, its documentation and its runtime setup. The authors formalize a repo-level code generation task whose input combines documentation, contextual dependencies…
  2. CodeDPO: Aligning Code Models with Self Generated and Verified Source Code (2025 · 20 citations)
    Supervised fine-tuning helps code LLMs, but it does not reliably teach them to favor correct programs over flawed ones. It also does nothing to reward faster code. CodeDPO addresses both gaps with preference learning.
  3. RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization (2026 · 6 citations)
    RL-PLUS targets a known weakness of Reinforcement Learning with Verifiable Reward (RLVR). Standard on-policy training such as GRPO raises pass@1 but often lowers pass@k at large k. In other words, the tuned model solves fewer distinct problems than its base model, which the authors call capability boundary collapse.…

All works

4 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

  1. Self-Edit: Fault-Aware Code Editor for Code Generation ↗Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetKechi Zhang et al.2023 · 105 citations

 Co-authors here

12 of 14 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 123 times (of the 149,439 papers whose bibliographies we read); 28 citing papers with a page here →.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/kechi-zhang/