1.5k citations · 1.6k across the 19 of their papers we have counts for
19 papers
Continual Learning in Transition
Zhiyan Hou, Dan Zhang, Tao Feng +11
Classical continual learning (CL) has primarily focused on enabling models to update and retain knowledge through parameter-centric mechanisms, e.g., training strategies, architect…
DASH: Divergence-Adaptive Supervision Horizons for On-Policy Self-Distillation of Reasoning Models
ZhiYan Hou, Xinyu Tang, Hongyan An +9
Reinforcement learning with verifiable rewards (RLVR) improves the reasoning capabilities of large language models using automatically verifiable outcome signals, but these signals…
ReLoop-UME: Recurrent Depth with Learnable Retrieval Registers for Universal Multimodal Embedding
Shijie Wang, Xiangzhao Hao, Yueti Li +3
Universal multimodal embedding (UME) maps heterogeneous multimodal inputs into a shared embedding space. Existing UME models either form embeddings through single forward encoding…
Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning
Xinyu Tang, Qianggang Cao, Yurou Liu +13
Reinforcement learning with verifiable rewards without human-annotated data, often referred to as zero RL, has emerged as a powerful paradigm for eliciting chain-of-thought reasoni…
GraphPO: Graph-based Policy Optimization for Reasoning Models
Yuliang Zhan, Xinyu Tang, Jian Li +7
Reinforcement Learning with Verifiable Rewards (RLVR) has become a standard paradigm for enhancing the capability of large reasoning models. RLVR typically samples responses indepe…
SearchSwarm: Towards Delegation Intelligence in Agentic LLMs for Long-Horizon Deep Research
Xiaochong Lan, Quan Chen, Kun Tao +7
Large language models are increasingly expected to handle complex, long-horizon real-world tasks whose context demands can grow without bound, yet model context windows remain inhe…
Rethinking Sample Polarity in Reinforcement Learning with Verifiable Rewards
Xinyu Tang, Yuliang Zhan, Zhixun Li +5
Large reasoning models (LRMs) are typically trained using reinforcement learning with verifiable reward (RLVR) to enhance their reasoning abilities. In this paradigm, policies are…
L2V-CoT: Cross-Modal Transfer of Chain-of-Thought Reasoning via Latent Intervention
Yuliang Zhan, Xinyu Tang, Han Wan +3
Recently, Chain-of-Thought (CoT) reasoning has significantly enhanced the capabilities of large language models (LLMs), but Vision-Language Models (VLMs) still struggle with multi-…
Towards High Data Efficiency in Reinforcement Learning with Verifiable Reward
Xinyu Tang, Zhenduo Zhang, Yurou Liu +4
Recent advances in large reasoning models have leveraged reinforcement learning with verifiable rewards (RLVR) to improve reasoning capabilities. However, scaling these methods typ…
Enhancing Cross-task Transfer of Large Language Models via Activation Steering
Xinyu Tang, Zhihao Lv, Xiaoxue Cheng +5
Large language models (LLMs) have shown impressive abilities in leveraging pretrained knowledge through prompting, but they often struggle with unseen tasks, particularly in data-s…
Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning
Xiaoxue Cheng, Junyi Li, Zhenduo Zhang +4
Large reasoning models (LRMs) have demonstrated strong performance on complex reasoning tasks, but often suffer from overthinking, generating redundant content regardless of task d…
Unlocking General Long Chain-of-Thought Reasoning Capabilities of Large Language Models via Representation Engineering
Xinyu Tang, Xiaolei Wang, Zhihao Lv +5
Recent advancements in long chain-of-thoughts(long CoTs) have significantly improved the reasoning capabilities of large language models(LLMs). Existing work finds that the capabil…
DAWN-ICL: Strategic Planning of Problem-solving Trajectories for Zero-Shot In-Context Learning
Xinyu Tang, Xiaolei Wang, Wayne Xin Zhao +1
Zero-shot in-context learning (ZS-ICL) aims to conduct in-context learning (ICL) without using human-annotated demonstrations. Most ZS-ICL methods use large language models (LLMs)…
YuLan: An Open-source Large Language Model
Yutao Zhu, Kun Zhou, Kelong Mao +35
Large language models (LLMs) have become the foundation of many applications, leveraging their extensive capabilities in processing and understanding natural language. While many o…
Investigating the Pre-Training Dynamics of In-Context Learning: Task Recognition vs. Task Learning
Xiaolei Wang, Xinyu Tang, Wayne Xin Zhao +1
The emergence of in-context learning (ICL) is potentially attributed to two major abilities: task recognition (TR) for recognizing the task from demonstrations and utilizing pre-tr…
Unleashing the Potential of Large Language Models as Prompt Optimizers: Analogical Analysis with Gradient-based Model Optimizers
Xinyu Tang, Xiaolei Wang, Wayne Xin Zhao +3
Automatic prompt optimization is an important approach to improving the performance of large language models (LLMs). Recent research demonstrates the potential of using LLMs as pro…
Improving Conversational Recommendation Systems via Counterfactual Data Simulation
Xiaolei Wang, Kun Zhou, Xinyu Tang +4
Conversational recommender systems (CRSs) aim to provide recommendation services via natural language conversations. Although a number of approaches have been proposed for developi…
Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models
Xiaolei Wang, Xinyu Tang, Wayne Xin Zhao +2
The recent success of large language models (LLMs) has shown great potential to develop more powerful conversational recommender systems (CRSs), which rely on natural language conv…
A Survey of Large Language Models
Wayne Xin Zhao, Kun Zhou, Junyi Li +19
Language is essentially a complex, intricate system of human expressions governed by grammatical rules. It poses a significant challenge to develop capable AI algorithms for compre…