4 citations · 5 across the 16 of their papers we have counts for
17 papers
Avoiding What Your Teacher Already Knows: Entity Familiarity for Deep-Search Distillation
Yifei Liu, Zhixiang Liang, Wentao Tan +4
Deep-search agents are commonly trained by distilling tool-use trajectories from stronger teacher models. However, structurally complex questions may remain relatively easy for tea…
OrthoSkillVLA: Continual Skill Learning via Gradient-Informed Skill Subspace Adaptation
Jiaqi Wang, Zhou Fang, Qiongfeng Shi +1
Pretrained Vision-Language-Action models provide a strong foundation for robot learning, but sequentially adapting them to diverse skills can perturb the representations and veloci…
FailForge: Distilling Procedural Competence from Persistent Failures into Code Agents
Dongyi Lv, Fushun E, Aichen Cai +8
Rejection sampling fine-tuning (RFT) is widely used to train code agents by generating trajectories on verifiable software engineering tasks, retaining those that pass the tests, a…
SearchAuditor: Auditing and Attributing Failures in Long-Horizon Search Agents
Zhixiang Liang, Yifei Liu, Yidan Huang +5
Deep search agents tackle challenging questions through long-horizon web interactions, a process that is both complex and fragile: small reasoning errors may propagate through long…
SearchMaster: Grounded and Regulated Self-Play for Search Agents
Wentao Tan, Qiong Cao, Jiaqi Wang +1
Training LLM-based search agents requires high-quality search data: tasks that demand genuine multi-hop retrieval and trajectories that use search tools effectively. Existing pipel…
DOPD: Dual On-policy Distillation
Xinlei Yu, Gen Li, Qingyi Si +13
On-policy distillation (OPD) offers superior capacity transfer by supervising student-sampled trajectories with dense token-level signals. To furnish high-quality supervision sourc…
Leveraging Error Diversity in Group Rollouts for Reinforcement Learning
Wenpu Liu, Yuqi Xu, Weichu Xie +8
Reinforcement Learning from Verifiable Rewards (RLVR) typically samples multiple responses per prompt and assigns binary rewards based on individual correctness, yet the collective…
Right Makes Might: Aligning Verified Hidden States Empowers RL Reasoning
Ziyue Wang, Aomufei Yuan, Yongfu Zhu +10
Reinforcement Learning from Verifiable Rewards (RLVR) has become the dominant approach for improving mathematical reasoning in large language models, yet current methods reduce eac…
Not only where, But when: Temporal Scheduling for RLVR
Jinghao Zhang, Ruilin Li, Feng Zhao +1
Reinforcement learning with verifiable rewards (RLVR) has become a core technique for post-training of Large Language Models (LLMs). While policy optimization is driven by all samp…
Step-wise Rubric Rewards for LLM Reasoning
Weichu Xie, Haozhe Zhao, Wenpu Liu +15
Reinforcement Learning with Verifiable Rewards (RLVR) is widely used to improve reasoning in large language models, but rewards only final-answer correctness with no supervision ov…
Co-Evolving Policy Distillation
Naibin Gu, Chenxu Yang, Qingyi Si +7
RLVR and OPD have become standard paradigms for post-training. We provide a unified analysis of these two paradigms in consolidating multiple expert capabilities into a single mode…
Learning from the Near Future: Temporal Self-Distillation for RLVR
Chuanyu Qin, Chenxu Yang, Qingyi Si +6
Reinforcement learning with verifiable rewards (RLVR) is a core post-training recipe for reasoning models, yet pure on-policy learning can be inefficient when useful trajectories a…
Self-Distilled RLVR
Chenxu Yang, Chuanyu Qin, Qingyi Si +7
On-policy distillation (OPD) has become a popular training paradigm in the LLM community. This paradigm selects a larger model as the teacher to provide dense, fine-grained signals…
ProbeFlow: Training-Free Adaptive Flow Matching for Vision-Language-Action Models
Zhou Fang, Jiaqi Wang, Yi Zhou +2
Vision-Language-Action (VLA) policies increasingly use Flow Matching (FM) action heads to generate action chunks as continuous trajectories. At inference, an FM head solves an ordi…
GCL-Sampler: Discovering Kernel Similarity for Sampled GPU Simulation via Graph Contrastive Learning
Jiaqi Wang, Jingwei Sun, Jiyu Luo +2
GPU architectural simulation is orders of magnitude slower than native execution, necessitating workload sampling for practical speedups. Existing methods rely on hand-crafted feat…
GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models
5 Team, Aohan Zeng, Xin Lv +167
We present GLM-4.5, an open-source Mixture-of-Experts (MoE) large language model with 355B total parameters and 32B activated parameters, featuring a hybrid reasoning method that s…
AutoGLM: Autonomous Foundation Agents for GUIs
Xiao Liu, Bo Qin, Dongzhu Liang +27
We present AutoGLM, a new series in the ChatGLM family, designed to serve as foundation agents for autonomous control of digital devices through Graphical User Interfaces (GUIs). W…