13 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…
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…
FSA: An Alternative Efficient Implementation of Native Sparse Attention Kernel
Ran Yan, Youhe Jiang, Zhuoming Chen +3
Recent advances in sparse attention mechanisms have demonstrated strong potential for reducing the computational cost of long-context training and inference in large language model…
AREAL-DTA: Dynamic Tree Attention for Efficient Reinforcement Learning of Large Language Models
Jiarui Zhang, Yuchen Yang, Ran Yan +8
Reinforcement learning (RL)-based post-training for large language models (LLMs) is computationally expensive, as it generates many rollout sequences that frequently share long tok…
D^2SD: Accelerating Speculative Decoding with Dual Diffusion Draft Models
Liyuan Zhang, Jiarui Zhang, Jinwei Yao +6
Speculative decoding accelerates autoregressive large language model inference by drafting multiple tokens and verifying them in a single target-model forward pass. Recent diffusio…
HexiScale: Facilitating Large Language Model Training over Heterogeneous Hardware
Ran Yan, Youhe Jiang, Xiaonan Nie +3
Training large language models (LLMs) is a computationally intensive task, which is typically conducted in data centers with homogeneous high-performance GPUs. In this paper, we ex…