2 citations · 2 across the 11 of their papers we have counts for
6 papers · 1 filter
State-OPD: State-Grounded On-Policy Distillation for Multi-Turn Agents
Zhentao Zhang, Yanzhao Zheng, Yanlin Liu +9
On-Policy Distillation (OPD) has emerged as a powerful paradigm for transferring reasoning capabilities from frontier models to smaller models, while extending its success to multi…
MOPD-Router: Rethinking Teacher Routing in Multi-Teacher On-Policy Distillation
Tianze Xu, Yanzhao Zheng, Zhentao Zhang +10
Multi-teacher on-policy distillation (MOPD) integrates specialized capabilities into a single student, but existing practice typically hard-routes each prompt to a domain-matched t…
Counterfactual Constraint-Conditioned On-Policy Distillation for Multi-Constraint Instruction Following
Yanzhao Zheng, Yuanqiang Yu, Tianze Xu +6
Multi-constraint instruction following requires a model to respond to a query under many simultaneously active constraints. Even strong instruction-tuned models still routinely vio…
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…
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…
SERL: Self-Examining Reinforcement Learning on Open-Domain
Weixuan Ou, Yanzhao Zheng, Shuoshuo Sun +7
Reinforcement Learning (RL) has been shown to improve the capabilities of large language models (LLMs). However, applying RL to open-domain tasks faces two key challenges: (1) the…