137 citations · 159 across the 18 of their papers we have counts for
20 papers
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…
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…
GANPrompt: Enhancing Robustness in LLM-Based Recommendations with GAN-Enhanced Diversity Prompts
Xinyu Li, Chuang Zhao, Hongke Zhao +2
In recent years, Large Language Models (LLMs) have demonstrated remarkable proficiency in comprehending and generating natural language, with a growing prevalence in the domain of…
LANE: Logic Alignment of Non-tuning Large Language Models and Online Recommendation Systems for Explainable Reason Generation
Hongke Zhao, Songming Zheng, Likang Wu +2
The explainability of recommendation systems is crucial for enhancing user trust and satisfaction. Leveraging large language models (LLMs) offers new opportunities for comprehensiv…