3 citations · 5 across the 16 of their papers we have counts for
17 papers
PCoMoE: Shifting MoE Inference from Monolithic Expert Selection to Fine-Grained Path Composition
Ziyan Gan, Fangxin Liu, Chenyang Guan +10
Mixture-of-Experts (MoE) architectures scale Large Language Model (LLM) capacity efficiently by activating a sparse subset of experts per token. However, modern MoE inference remai…
Rollplex: Cross-Phase GPU Spatial Sharing for Vision Language Model Post-Training
Hanfeng Lu, Tianyu Feng, Suyi Li +8
Vision-language models (VLMs) enable embodied agents to reason and act from visual observations and language instructions. Reinforcement learning (RL) post-training enhances these…
ROSE: Rollout On Serving GPUs via Cooperative Elasticity for Agentic RL
Wei Gao, Yuheng Zhao, Dilxat Muhtar +13
Agentic reinforcement learning (RL) is reshaping LLM post-training, but end-to-end training time is dominated by compute-intensive, multi-turn rollouts whose resource demand varies…
Complementary RL: Towards Efficient Experience-Driven Agent Learning
Dilxat Muhtar, Jiashun Liu, Wei Gao +8
Reinforcement Learning (RL) has emerged as a powerful paradigm for training LLM-based agents, yet remains limited by low sample efficiency, stemming not only from sparse outcome fe…
AMAP Agentic Planning Technical Report
AMAP AI Agent Team, Yulan Hu, Xiangwen Zhang +22
We present STAgent, an agentic large language model tailored for spatio-temporal understanding, designed to solve complex tasks such as constrained point-of-interest discovery and…
RollMux: Phase-Level Multiplexing for Disaggregated RL Post-Training
Tianyuan Wu, Lunxi Cao, Yining Wei +11
Rollout-training disaggregation is emerging as the standard architecture for Reinforcement Learning (RL) post-training, where memory-bound rollout and compute-bound training are ph…
Let It Flow: Agentic Crafting on Rock and Roll, Building the ROME Model within an Open Agentic Learning Ecosystem
Weixun Wang, XiaoXiao Xu, Wanhe An +86
Agentic crafting requires LLMs to operate in real-world environments over multiple turns by taking actions, observing outcomes, and iteratively refining artifacts. Despite its impo…
RollArt: Disaggregated Multi-Task Agentic RL Training at Scale
Wei Gao, Yuheng Zhao, Tianyuan Wu +15
Agentic Reinforcement Learning (RL) trains LLMs through multi-turn interactions with environments, producing workloads that mix compute-bound prefill, bandwidth-bound decoding, CPU…
Reconstructing KV Caches with Cross-layer Fusion For Enhanced Transformers
Hongzhan Lin, Zhiqi Bai, Xinmiao Zhang +10
Transformer decoders have achieved strong results across tasks, but the memory required for the KV cache becomes prohibitive at long sequence lengths. Although Cross-layer KV Cache…
Part II: ROLL Flash -- Accelerating RLVR and Agentic Training with Asynchrony
Han Lu, Zichen Liu, Shaopan Xiong +19
Synchronous Reinforcement Learning (RL) post-training has emerged as a crucial step for enhancing Large Language Models (LLMs) with diverse capabilities. However, many systems desi…
RollPacker: Mitigating Long-Tail Rollouts for Fast, Synchronous RL Post-Training
Wei Gao, Yuheng Zhao, Dakai An +11
Reinforcement Learning (RL) is a pivotal post-training technique for enhancing the reasoning capabilities of Large Language Models (LLMs). However, synchronous RL post-training oft…
RecIS: Sparse to Dense, A Unified Training Framework for Recommendation Models
Hua Zong, Qingtao Zeng, Zhengxiong Zhou +31
In this paper, we propose RecIS, a unified Sparse-Dense training framework designed to achieve two primary goals: 1. Unified Framework To create a Unified sparse-dense training fra…
Part I: Tricks or Traps? A Deep Dive into RL for LLM Reasoning
Zihe Liu, Jiashun Liu, Yancheng He +13
Reinforcement learning for LLM reasoning has rapidly emerged as a prominent research area, marked by a significant surge in related studies on both algorithmic innovations and prac…
Adaptra: Straggler-Resilient Hybrid-Parallel Training with Pipeline Adaptation
Tianyuan Wu, Lunxi Cao, Hanfeng Lu +8
Training large Deep Neural Network (DNN) models at scale often encounters straggler issues, mostly in communications due to network congestion, RNIC/switch defects, or topological…
CombatVLA: An Efficient Vision-Language-Action Model for Combat Tasks in 3D Action Role-Playing Games
Peng Chen, Pi Bu, Yingyao Wang +9
Recent advances in Vision-Language-Action models (VLAs) have expanded the capabilities of embodied intelligence. However, significant challenges remain in real-time decision-making…
FALCON: Pinpointing and Mitigating Stragglers for Large-Scale Hybrid-Parallel Training
Tianyuan Wu, Wei Wang, Yinghao Yu +7
Fail-slows, or stragglers, are common but largely unheeded problems in large-scale hybrid-parallel training that spans thousands of GPU servers and runs for weeks to months. Yet, t…
Enhancing Large-Scale AI Training Efficiency: The C4 Solution for Real-Time Anomaly Detection and Communication Optimization
Jianbo Dong, Bin Luo, Jun Zhang +22
The emergence of Large Language Models (LLMs) has necessitated the adoption of distributed training techniques, involving the deployment of thousands of GPUs to train a single mode…