15 citations · 22 across the 10 of their papers we have counts for
5 papers · 1 filter
PreSO: Privileged Representation Isolation for On-Policy Self-Distillation
Yuhang Yang, Mingxuan Xia, Chao Ye +6
On-policy self-distillation (OPSD) uses a self-teacher conditioned on privileged information to turn the sparse outcome signal of reinforcement learning with verifiable rewards (RL…
CriPO: Enhancing Rubric-based RL via Self-Distillation
Mingxuan Xia, Yuhang Yang, Chao Ye +8
Rubric-based Reinforcement Learning (RL) has recently shown promise in improving Large Language Models (LLMs) on open-ended tasks. A widely recognized limitation of rubric-based RL…
Everyone is unique: Towards Behaviorally Heterogeneous Negotiation Dialogue Systems for Debt Collection
Yuhang Yang, Kai Tang, Chao Ye +4
Debt collection is a critical negotiation task in the financial industry, with strong practical relevance and exceptional academic value as a behaviorally rich, high-stakes testbed…
Optimus: A Generic Operator-Level PyTorch Model Transformation Framework
Menglu Yu, Jiaqi Xu, Yuzhen Huang +19
In large-scale industrial applications, deep learning models that power recommendation and ranking have complex and diverse model architectures. These models are continuously devel…
Kunlun: Establishing Scaling Laws for Massive-Scale Recommendation Systems through Unified Architecture Design
Bojian Hou, Xiaolong Liu, Xiaoyi Liu +26
Deriving predictable scaling laws that govern the relationship between model performance and computational investment is crucial for designing and allocating resources in massive-s…