5 papers · 1 filter
TRAM: Enhancing Multimodal Reasoning with Trajectory-Derived Auxiliary Memory
Kang Liu, Zijing Wang, Yongkang Liu +5
Multimodal Large Reasoning Models (MLRMs) have achieved strong performance on tasks requiring visual understanding and multi-step inference. However, as reasoning trajectories grow…
DiM\textsuperscript{3}: Bridging Multilingual and Multimodal Models via Direction- and Magnitude-Aware Merging
Zijing Wang, Mingyang Wang, Ercong Nie +6
Towards more general and human-like intelligence, large language models should seamlessly integrate both multilingual and multimodal capabilities; however, extending an existing mu…
A Systematic Analysis of the Impact of Persona Steering on LLM Capabilities
Jiaqi Chen, Ming Wang, Tingna Xie +2
Imbuing Large Language Models (LLMs) with specific personas is prevalent for tailoring interaction styles, yet the impact on underlying cognitive capabilities remains unexplored. W…
Every Step Evolves: Scaling Reinforcement Learning for Trillion-Scale Thinking Model
Ling Team, Anqi Shen, Baihui Li +101
We present Ring-1T, the first open-source, state-of-the-art thinking model with a trillion-scale parameter. It features 1 trillion total parameters and activates approximately 50 b…
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…