3 citations · 6 across the 25 of their papers we have counts for
15 papers · 1 filter
Grokked Models are Better Unlearners
Yuanbang Liang, Yang Li
Grokking-delayed generalization that emerges well after a model has fit the training data-has been linked to robustness and representation quality. We ask whether this training reg…
Adapting Like Humans: A Metacognitive Agent with Test-time Reasoning
Yang Li, Zhiyuan He, Yuxuan Huang +5
Recent Vision-Language Models (VLMs) exhibit strong perceptual reasoning abilities, yet they often struggle to adapt efficiently when encountering novel tasks at test time. In cont…
ELPO: Ensemble Learning Based Prompt Optimization for Large Language Models
Qing Zhang, Bing Xu, Xudong Zhang +9
The remarkable performance of Large Language Models (LLMs) highly relies on crafted prompts. However, manual prompt engineering is a laborious process, creating a core bottleneck f…
MarsRL: Advancing Multi-Agent Reasoning System via Reinforcement Learning with Agentic Pipeline Parallelism
Shulin Liu, Dong Du, Tao Yang +2
Recent progress in large language models (LLMs) has been propelled by reinforcement learning with verifiable rewards (RLVR) and test-time scaling. However, the limited output lengt…
Thinker: Training LLMs in Hierarchical Thinking for Deep Search via Multi-Turn Interaction
Jun Xu, Xinkai Du, Yu Ao +17
Efficient retrieval of external knowledge bases and web pages is crucial for enhancing the reasoning abilities of LLMs. Previous works on training LLMs to leverage external retriev…
AgentGit: A Version Control Framework for Reliable and Scalable LLM-Powered Multi-Agent Systems
Yang Li, Siqi Ping, Xiyu Chen +4
With the rapid progress of large language models (LLMs), LLM-powered multi-agent systems (MAS) are drawing increasing interest across academia and industry. However, many current M…
Forecasting the Buzz: Enriching Hashtag Popularity Prediction with LLM Reasoning
Yifei Xu, Jiaying Wu, Herun Wan +3
Hashtag trends ignite campaigns, shift public opinion, and steer millions of dollars in advertising spend, yet forecasting which tag goes viral is elusive. Classical regressors dig…
Self-Evolving Vision-Language Models for Image Quality Assessment via Voting and Ranking
Wen Wen, Tianwu Zhi, Kanglong Fan +6
Improving vision-language models (VLMs) in the post-training stage typically relies on supervised fine-tuning or reinforcement learning, methods that necessitate costly, human-anno…
FACTORY: A Challenging Human-Verified Prompt Set for Long-Form Factuality
Mingda Chen, Yang Li, Xilun Chen +3
Long-form factuality evaluation assesses the ability of models to generate accurate, comprehensive responses to short prompts. Existing benchmarks often lack human verification, le…
UloRL:An Ultra-Long Output Reinforcement Learning Approach for Advancing Large Language Models' Reasoning Abilities
Dong Du, Shulin Liu, Tao Yang +2
Recent advances in large language models (LLMs) have highlighted the potential of reinforcement learning with verifiable rewards (RLVR) to enhance reasoning capabilities through ex…
Skip a Layer or Loop it? Test-Time Depth Adaptation of Pretrained LLMs
Ziyue Li, Yang Li, Tianyi Zhou
Can a pretrained neural network adapt its architecture to different inputs without any finetuning? Do we need all layers for simple tasks, and are they adequate for challenging tas…
Ring-lite: Scalable Reasoning via C3PO-Stabilized Reinforcement Learning for LLMs
Ling Team, Bin Hu, Cai Chen +43
We present Ring-lite, a Mixture-of-Experts (MoE)-based large language model optimized via reinforcement learning (RL) to achieve efficient and robust reasoning capabilities. Built…
Rethinking Predictive Modeling for LLM Routing: When Simple kNN Beats Complex Learned Routers
Yang Li
As large language models (LLMs) grow in scale and specialization, routing--selecting the best model for a given input--has become essential for efficient and effective deployment.…
Policy Guided Tree Search for Enhanced LLM Reasoning
Yang Li
Despite their remarkable capabilities, large language models often struggle with tasks requiring complex reasoning and planning. While existing approaches like Chain-of-Thought pro…
LLM Bandit: Cost-Efficient LLM Generation via Preference-Conditioned Dynamic Routing
Yang Li
The rapid advancement in large language models (LLMs) has brought forth a diverse range of models with varying capabilities that excel in different tasks and domains. However, sele…