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20212026
most citedWizardLM: Empowering large pre-trained language models to follow complex instructions

109 citations · 116 across the 10 of their papers we have counts for

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cs.AI2026

Hunyuan-A13B Technical Report

Tencent Hunyuan Team, Ao Liu, Botong Zhou +72

We present Hunyuan-A13B, an open-source large language model based on a Mixture-of-Experts architecture. It contains 80 billion total parameters but activates only 13 billion durin…

cs.AI2026

TurnOPD: Making On-Policy Distillation Turn-Aware for Efficient Long-Horizon Agent Training

Yuhang Zhou, Kai Zheng, Haoling Li +3

On-policy distillation (OPD) trains a student policy by matching a stronger teacher on the student's own trajectories, offering a promising framework for language agent training. H…

cs.AI2026

VeriEvol: Scaling Multimodal Mathematical Reasoning via Verifiable Evol-Instruct

Haoling Li, Kai Zheng, Jie Wu +4

Scaling reinforcement learning for visual mathematical reasoning requires more than generating harder questions: as data volume grows, the reward labels themselves must remain reli…

cs.AI2026

RubricBench: Aligning Model-Generated Rubrics with Human Standards

Qiyuan Zhang, Junyi Zhou, Yufei Wang +8

As Large Language Model (LLM) alignment evolves from simple completions to complex, highly sophisticated generation, Reward Models are increasingly shifting toward rubric-guided ev…

cs.AI2026

Beyond Length Scaling: Synergizing Breadth and Depth for Generative Reward Models

Qiyuan Zhang, Yufei Wang, Tianhe Wu +5

Recent advancements in Generative Reward Models (GRMs) have demonstrated that scaling the length of Chain-of-Thought (CoT) reasoning considerably enhances the reliability of evalua…