1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.CL2025
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…
cs.CL2025★ 1 cited
LongCat-Flash Technical Report
Meituan LongCat Team, Bayan, Bei Li +179
We introduce LongCat-Flash, a 560-billion-parameter Mixture-of-Experts (MoE) language model designed for both computational efficiency and advanced agentic capabilities. Stemming f…
cs.CL2025
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…