◍wovepaper
SearchResearchersInstitutions
Sign in
researcher

Debing Zhang

9 papers hereh-index 591 citations10 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author6
  • last author3

Across the 9 of 9 papers where every author was matched, so the position is known.

fields
  • cs.CL5
  • cs.LG2
  • cs.AI1
  • cs.CV1
same name
  • Debing Zhang — 14 papers, h 7
  • Debing Zhang — 9 papers, h 7
  • Debing Zhang — 4 papers, h 4
  • Debing Zhang — 3 papers, h 14
  • Debing Zhang — 3 papers, h 3
  • Debing Zhang — 1 paper

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20242026
most citedCritic-CoT: Boosting the reasoning abilities of large language model via Chain-of-thoughts Critic

1 citations · 1 across the 9 of their papers we have counts for

collaborators
Showing 2025 · cs.CLShow all

4 papers · 2 filters

cs.CL2025

PretrainZero: Reinforcement Active Pretraining

Xingrun Xing, Zhiyuan Fan, Jie Lou +3

Mimicking human behavior to actively learning from general experience and achieve artificial general intelligence has always been a human dream. Recent reinforcement learning (RL)…

cs.CL2025

Coupled Variational Reinforcement Learning for Language Model General Reasoning

Xueru Wen, Jie Lou, Yanjiang Liu +6

While reinforcement learning has achieved impressive progress in language model reasoning, it is constrained by the requirement for verifiable rewards. Recent verifier-free RL meth…

cs.CL2025

MemSearcher: Training LLMs to Reason, Search and Manage Memory via End-to-End Reinforcement Learning

Qianhao Yuan, Jie Lou, Zichao Li +6

LLM-based search agents often concatenate the full interaction history into the context, producing long and noisy inputs, and increasing compute cost and GPU memory overhead. To ad…

cs.CL2025

Cheems: A Practical Guidance for Building and Evaluating Chinese Reward Models from Scratch

Xueru Wen, Jie Lou, Zichao Li +9

Reward models (RMs) are crucial for aligning large language models (LLMs) with human preferences. However, most RM research is centered on English and relies heavily on synthetic r…

◍wovepaper

Papers, researchers and institutions, woven together.

Explore
  • Search
  • Researchers
  • Institutions
Account
  • Library
  • Chat
Data
  • arXiv.org
  • Semantic Scholar
  • OpenAlex
  • Latest RSS
AboutContactPrivacyDevelopersllms.txtopenapi.json
Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.