26 citations · 104 across the 32 of their papers we have counts for
6 papers · 1 filter
From Understanding to Action: Feedback-Grounded Policy Discovery for Generative Recommendation
Zhi Chen, Minmao Wang, Xingchen Liu +8
Semantic-ID-based generative recommenders enable efficient next-item generation, but their item-level supervision mainly captures behavioral co-occurrence and local transitions. La…
From Trajectories to Prefixes: Reusing Teacher Trajectories via Replayed Prefixes and Online Continuation
Yihan Wang, Zhong Guan, Haoran Sun +3
Small language models are attractive backbones for interactive agents, but direct distillation from strong teacher trajectories often turns rich multi-turn behavior into one-shot i…
Robust Contrastive Graph Clustering with Adaptive Local-Global Integration
Lei Zhang, Fubo Sun, Haipeng Yang +2
Graph clustering is essential in graph analysis for revealing structural patterns and node communities. Despite recent advances in self-supervised contrastive learning that have im…
GCIB: Graph Contrastive Information Bottleneck for Multi-Behavior Recommendation
Likang Wu, Zihao Chen, Jianxin Zhang +4
With the rapid emergence of multi-behavior learning in recommender systems, leveraging auxiliary user behaviors has proven effective for mitigating target-behavior data sparsity. Y…
Missing Old Logits in Asynchronous Agentic RL: Semantic Mismatch and Repair Methods for Off-Policy Correction
Zhong Guan, Yongjian Guo, Haoran Sun +5
Asynchronous reinforcement learning improves rollout throughput for large language model agents by decoupling sample generation from policy optimization, but it also introduces a c…
RL-VLA: A Flexible and Asynchronous Reinforcement Learning Framework for VLA Training
Haoran Sun, Yongjian Guo, Zhong Guan +13
Reinforcement learning (RL) has emerged as a critical paradigm for post-training Vision-Language-Action (VLA) models, enabling embodied agents to adapt and improve through environm…