9 accepted papers
Recent work increasingly synthesizes agentic tasks for post-training tool-using LLMs, yet robust generalization under shifts in tasks and toolsets remains an open challenge. We trace this brittleness to insufficient diversity in synthesized training tasks. Scaling diversity is difficult because trai…
Offline reinforcement learning (Offline RL) provides a compelling solution for applying RL in high-risk or resourceconstrained real-world domains such as healthcare, autonomous driving, and robotic manipulation, where online exploration can be unsafe or impractical. However, Offline RL faces critica
Offline reinforcement learning (Offline RL) provides a compelling solution for applying RL in high-risk or resource-constrained real-world domains such as healthcare, autonomous driving, and robotic manipulation. However, Offline RL faces critical challenges arising from limited data coverage and po…
Recent advances such as OpenAI-o1 and DeepSeek R1 have demonstrated the potential of Reinforcement Learning (RL) to enhance reasoning abilities in Large Language Models (LLMs). While open-source replication efforts have primarily focused on mathematical and coding domains, methods and resources for…
Recently, deep learning models have been widely explored in recommender systems. Though having achieved remarkable success, the design of task-aware recommendation models usually requires manual feature engineering and architecture engineering from domain experts. To relieve those efforts, we explor…
Recently, sequential recommendation has become a significant demand for many real-world applications, where the recommended items would be displayed to users one after another and the order of the displays influences the satisfaction of users. An extensive number of models have been developed for se…
Knowledge distillation has become increasingly important in model compression. It boosts the performance of a miniaturized student network with the supervision of the output distribution and feature maps from a sophisticated teacher network. Some recent works introduce multi-teacher distillation to…
User profiles are not always visible in E-commerce scenarios, in which case the recommender systems can only summarize users' preferences through sessions of historical records. However, the items in a session might be irrelevant to users' preferences or become the disturbances for modelling the use…
In the past few years, virtual reality (VR) has become an enabling technique, not only for enriching our visual experience but also for providing new channels for businesses. Untethered mobile devices are the main players for watching 360-degree content, thereby the precision of predicting the futur…