4 papers
Trust, But Verify: A Self-Verification Approach to Reinforcement Learning with Verifiable Rewards
Xiaoyuan Liu, Tian Liang, Zhiwei He +6
Large Language Models (LLMs) show great promise in complex reasoning, with Reinforcement Learning with Verifiable Rewards (RLVR) being a key enhancement strategy. However, a preval…
The First Few Tokens Are All You Need: An Efficient and Effective Unsupervised Prefix Fine-Tuning Method for Reasoning Models
Ke Ji, Jiahao Xu, Tian Liang +10
Improving the reasoning capabilities of large language models (LLMs) typically requires supervised fine-tuning with labeled data or computationally expensive sampling. We introduce…
Can't See the Forest for the Trees: Benchmarking Multimodal Safety Awareness for Multimodal LLMs
Wenxuan Wang, Xiaoyuan Liu, Kuiyi Gao +5
Multimodal Large Language Models (MLLMs) have expanded the capabilities of traditional language models by enabling interaction through both text and images. However, ensuring the s…
AI Sees Your Location, But With A Bias Toward The Wealthy World
Jingyuan Huang, Jen-tse Huang, Ziyi Liu +3
Visual-Language Models (VLMs) have shown remarkable performance across various tasks, particularly in recognizing geographic information from images. However, VLMs still show regio…