3 papers
cs.RO2026
GPU-Parallel Multi-Task Reinforcement Learning with Demonstration Guided Policy Optimization
Rui Zhang, Qiwei Wu, Zhengyu Zhang +5
Large scale GPU-parallel reinforcement learning has changed what can be trained in robot simulation, yet most systems still optimize one specialist policy per task. We propose a co…
cs.LG2026
FP8-RL: A Practical and Stable Low-Precision Stack for LLM Reinforcement Learning
Zhaopeng Qiu, Shuang Yu, Jingqi Zhang +4
Reinforcement learning (RL) for large language models (LLMs) is increasingly bottlenecked by rollout (generation), where long output sequence lengths make attention and KV-cache me…
cs.LG2026
AIConfigurator: Lightning-Fast Configuration Optimization for Multi-Framework LLM Serving
Tianhao Xu, Yiming Liu, Xianglong Lu +18
Optimizing Large Language Model (LLM) inference in production systems is increasingly difficult due to dynamic workloads, stringent latency/throughput targets, and a rapidly expand…