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Jujie He

7 papers hereh-index 6729 citations9 works total

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

author position
  • first author1
  • middle author6

Across the 7 of 7 papers where every author was matched, so the position is known.

fields
  • cs.AI4
  • cs.CL2
  • cs.LG1
same name
  • Jujie He — 1 paper, h 2
  • Jujie He — 1 paper
  • Jujie He — 1 paper, h 0

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 citedQ*: Improving Multi-step Reasoning for LLMs with Deliberative Planning

9 citations · 11 across the 7 of their papers we have counts for

collaborators
Showing cs.AIShow all

4 papers · 1 filter

cs.AI2025

Skywork-SWE: Unveiling Data Scaling Laws for Software Engineering in LLMs

Liang Zeng, Yongcong Li, Yuzhen Xiao +8

Software engineering (SWE) has recently emerged as a crucial testbed for next-generation LLM agents, demanding inherent capabilities in two critical dimensions: sustained iterative…

cs.AI2024★ 1 cited

Skywork-Reward: Bag of Tricks for Reward Modeling in LLMs

Chris Yuhao Liu, Liang Zeng, Jiacai Liu +6

In this report, we introduce a collection of methods to enhance reward modeling for LLMs, focusing specifically on data-centric techniques. We propose effective data selection and…

cs.AI2024★ 9 cited

Q*: Improving Multi-step Reasoning for LLMs with Deliberative Planning

Chaojie Wang, Yanchen Deng, Zhiyi Lyu +4

Large Language Models (LLMs) have demonstrated impressive capability in many natural language tasks. However, the auto-regressive generation process makes LLMs prone to produce err…

cs.AI2024

Skywork-Math: Data Scaling Laws for Mathematical Reasoning in Large Language Models -- The Story Goes On

Liang Zeng, Liangjun Zhong, Liang Zhao +9

In this paper, we investigate the underlying factors that potentially enhance the mathematical reasoning capabilities of large language models (LLMs). We argue that the data scalin…

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