1 citations · 1 across the 8 of their papers we have counts for
8 papers
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…
Crab: A Semantics-Aware Checkpoint/Restore Runtime for Agent Sandboxes
Tianyuan Wu, Chaokun Chang, Lunxi Cao +2
Autonomous agents act through sandboxed containers and microVMs whose state spans filesystems, processes, and runtime artifacts. Checkpoint and restore (C/R) of this state is neede…
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…
AReaL-Hex: Accommodating Asynchronous RL Training over Heterogeneous GPUs
Ran Yan, Youhe Jiang, Tianyuan Wu +7
Maximizing training throughput and cost-efficiency of RL for LLMs is essential to democratize this advanced technique. One promising but challenging approach is to deploy such a co…
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…
Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library
Weixun Wang, Shaopan Xiong, Gengru Chen +38
We introduce ROLL, an efficient, scalable, and user-friendly library designed for Reinforcement Learning Optimization for Large-scale Learning. ROLL caters to three primary user gr…