5 papers
AReaL-DTE: Sparse Policy-Weight Transfer for Online Agentic Reinforcement Learning
Yingqi Peng, Jiawei Zhang, Wenhao Zhou +7
Online agentic reinforcement learning implemented with micro-services separates policy training from rollout generation, improving scalability and modularity while potentially maki…
DualDecoder: Accelerate Long Context LLM Inference by Predictive Prefetch
Zuning Liang, Zhiyi Yao, Qi Chen +6
Long-context inference is becoming a fundamental capability for modern LLM serving, especially driven by emerging agentic applications. Yet it faces a severe memory wall that the K…
Next-Generation Agentic Reinforcement Learning Systems Enable Self-Evolving Agents
Ran Yan, Wei Fu, Jiale Li +21
LLM agents are rapidly being deployed in production, including coding assistants, customer-support chatbots, and scientific research assistants, yet they remain fundamentally stati…
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…
Improved Clifford operations in constant commutative depth
Richard Cleve, Zhiqian Ding, Luke Schaeffer
The commutative depth model allows gates that commute with each other to be performed in parallel. We show how to compute Clifford operations in constant commutative depth more eff…