most citedPerson-Job Fit: Adapting the Right Talent for the Right Job with Joint Representation Learning

23 citations · 23 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2026

PAC: Progress-Augmented Advantage Curriculum for Multi-Task Reinforcement Learning of LLMs

Yuanqiang Yu, Yanzhao Zheng, Zhentao Zhang +8

Reinforcement learning (RL) is used to improve the reasoning abilities of LLMs, while training data span heterogeneous tasks. However, most RL post-training pipelines rely on fixed…

cs.CL2026

Rubrics to Tokens: Bridging Response-level Rubrics and Token-level Rewards in Instruction Following Tasks

Tianze Xu, Yanzhao Zheng, Pengrui Lu +11

Rubric-based Reinforcement Learning (RL) has emerged as a promising approach for aligning Large Language Models (LLMs) with complex, open-domain instruction following tasks. Howeve…

cs.AI2026

ContextBudget: Budget-Aware Context Management for Long-Horizon Search Agents

Yong Wu, YanZhao Zheng, TianZe Xu +9

LLM-based agents show strong potential for long-horizon reasoning, yet their context size is limited by deployment factors (e.g., memory, latency, and cost), yielding a constrained…

cs.LG2026

SkillRouter: Skill Routing for LLM Agents at Scale

YanZhao Zheng, ZhenTao Zhang, Chao Ma +8

Reusable skills let LLM agents package task-specific procedures, tool affordances, and execution guidance into modular building blocks. As skill ecosystems grow to tens of thousand…

cs.IR2018★ 23 cited

Person-Job Fit: Adapting the Right Talent for the Right Job with Joint Representation Learning

Chen Zhu, Hengshu Zhu, Hui Xiong +4

Person-Job Fit is the process of matching the right talent for the right job by identifying talent competencies that are required for the job. While many qualitative efforts have b…