Qintong Wu

RAG-R1:Incentivizing the Search and Reasoning Capabilities of LLMs Through Multi-Query Parallelism

Zhiwen Tan, Jiaming Huang, Qintong Wu, Hongxuan Zhang et autres

Large Language Models (LLMs), despite their remarkable capabilities, are prone to generating hallucinated or outdated content due to their static internal knowledge. While Retrieval-Augmented Generation (RAG) integrated with Reinforcement Learning (RL) offers a solution, these methods are fundamentally constrained by a single-query …

cn, fr (code pays fourni par la source)

RAG-R1:Incentivizing the Search and Reasoning Capabilities of LLMs Through Multi-Query Parallelism

Association for Artificial Intelligence 2026, Jinjie Gu, Jiaming Huang, Zhiwen Tan et autres

Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks, while they remain prone to generating hallucinated or outdated responses due to their static internal knowledge. Recent advancements in Retrieval-Augmented Generation (RAG) methods have aimed to enhance models' search and reasoning …

Don't Just Fine-tune the Agent, Tune the Environment

Siyuan Lu, Zilu Wang, Hongxuan Zhang, Qintong Wu et autres

Large Language Model (LLM) agents show great promise for complex, multi-turn tool-use tasks, but their development is often hampered by the extreme scarcity of high-quality training data. Supervised fine-tuning (SFT) on synthetic data leads to overfitting, whereas standard reinforcement learning (RL) struggles …

RAG-R1: Incentivizing the Search and Reasoning Capabilities of LLMs through Multi-query Parallelism

Jiaming Huang, Qintong Wu, Hongxuan Zhang, Chenyi Zhuang et autres

Large Language Models (LLMs), despite their remarkable capabilities, are prone to generating hallucinated or outdated content due to their static internal knowledge. While Retrieval-Augmented Generation (RAG) integrated with Reinforcement Learning (RL) offers a solution, these methods are fundamentally constrained by a single-query …

Leave No One Behind: Online Self-Supervised Self-Distillation for Sequential Recommendation

Shaowei Wei, Zhengwei Wu, Xin Li, Qintong Wu et autres

cn (code pays fourni par la source)

Leave No One Behind: Online Self-Supervised Self-Distillation for Sequential Recommendation

Shaowei Wei, Zhengwei Wu, Xin Yan Li, Qintong Wu et autres

Sequential recommendation methods play a pivotal role in modern recommendation systems. A key challenge lies in accurately modeling user preferences in the face of data sparsity. To tackle this challenge, recent methods leverage contrastive learning (CL) to derive self-supervision signals by maximizing …

FwSeqBlock: A Field-wise Approach for Modeling Behavior Representation in Sequential Recommendation

Hao Qian, Qintong Wu, MingHao Li, Zhengwei Wu et autres

Modeling users' historical behaviors is an essential task in many industrial recommender systems. The user interest representation, in previous works, is obtained through the following paradigm: concrete behaviors are firstly embedded as low-dimensional behavior representations, which are then aggregated conditioning on the …

cn (code pays fourni par la source)

An Industrial Framework for Cold-Start Recommendation in Zero-Shot Scenarios

Zhaoxin Huan, Gongduo Zhang, Xiaolu Zhang, Jun Zhou et autres

There exists the cold-start problem in the recommendation systems when observed user-item interactions are insufficient. To alleviate this problem, most existing works aim to learn globally shared prior knowledge across all items and be fast adapted to a new item with few …

cn (code pays fourni par la source)

A Multi-Task Learning Approach for Delayed Feedback Modeling

Zhigang Huangfu, Gong-Duo Zhang, Zhengwei Wu, Qintong Wu et autres

Conversion rate (CVR) prediction is one of the most essential tasks for digital display advertising. In industrial recommender systems, online learning is particularly favored for its capability to capture the dynamic change of data distribution, which often leads to significantly improvement of …

cn (code pays fourni par la source)

Scope-aware Re-ranking with Gated Attention in Feed

Hao Qian, Qintong Wu, Peiyan Zhang, Zhiqiang Zhang et autres

Modern recommendation systems introduce the re-ranking stage to optimize the entire list directly. This paper focuses on the design of re-ranking framework in feed to optimally model the mutual influence between items and further promote user engagement. On mobile devices, users browse …

cn (code pays fourni par la source)