6 papers
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…
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…
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…
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…
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…