14 citations · 57 across the 29 of their papers we have counts for
3 papers · 1 filter
Ask to Distill: Learning to Synthesize Data for On-Policy Distillation
Jiashuo Sun, Hao Zhang, Xueqiang Xu +9
On-policy distillation (OPD) can succeed with surprisingly few training questions, motivating a view of questions as carriers of distributional knowledge transfer. How can we asses…
SIPO: Unifying Reinforcement Learning with On-Policy Self-Distillation
Zhenrui Yue, Huimin Zeng, Yueqi Wang +8
Reinforcement learning with verifiable rewards (RLVR) has become a standard paradigm for improving large language models (LLMs) on various tasks, yet its sparse outcome rewards lac…
Dr. Zero: Self-Evolving Search Agents without Training Data
Zhenrui Yue, Kartikeya Upasani, Xianjun Yang +5
As high-quality data becomes increasingly difficult to obtain, self-evolution without curated training data has emerged as a promising paradigm. This approach allows large language…