6 papers
Active Zero: Self-Evolving Vision-Language Models through Active Environment Exploration
Jinghan He, Junfeng Fang, Feng Xiong +5
Self-play has enabled large language models to autonomously improve through self-generated challenges. However, existing self-play methods for vision-language models rely on passiv…
On Predictability of Reinforcement Learning Dynamics for Large Language Models
Yuchen Cai, Ding Cao, Xin Xu +7
Recent advances in reasoning capabilities of large language models (LLMs) are largely driven by reinforcement learning (RL), yet the underlying parameter dynamics during RL trainin…
We Should Identify and Mitigate Third-Party Safety Risks in MCP-Powered Agent Systems
Junfeng Fang, Zijun Yao, Ruipeng Wang +3
The development of large language models (LLMs) has entered in a experience-driven era, flagged by the emergence of environment feedback-driven learning via reinforcement learning…
Steering LVLMs via Sparse Autoencoder for Hallucination Mitigation
Zhenglin Hua, Jinghan He, Zijun Yao +4
Large vision-language models (LVLMs) have achieved remarkable performance on multimodal tasks. However, they still suffer from hallucinations, generating text inconsistent with vis…
Toward Generalizable Evaluation in the LLM Era: A Survey Beyond Benchmarks
Yixin Cao, Shibo Hong, Xinze Li +24
Large Language Models (LLMs) are advancing at an amazing speed and have become indispensable across academia, industry, and daily applications. To keep pace with the status quo, th…
SafeMLRM: Demystifying Safety in Multi-modal Large Reasoning Models
Junfeng Fang, Yukai Wang, Ruipeng Wang +5
The rapid advancement of multi-modal large reasoning models (MLRMs) -- enhanced versions of multimodal language models (MLLMs) equipped with reasoning capabilities -- has revolutio…