1 citations · 1 across the 3 of their papers we have counts for
5 papers
Nereus: Adaptive Parallelism for LLM Post-Training
Songlin Jiang, Tuo Shi, Sitong Zhang +3
Reinforcement learning (RL) post-training for large language models (LLMs) coordinates multiple models across generation, inference, and training on GPU clusters. Several factors m…
Conduit: An Experience Data Plane for Distributed Reinforcement Learning
Sitong Zhang, Tuo Shi, Mario Di Francesco +2
Distributed reinforcement learning (RL) scales training by parallelizing actors and learners around an Experience Buffer. As RL workloads grow, however, the buffer becomes more tha…
EARL: Efficient Agentic Reinforcement Learning Systems for Large Language Models
Zheyue Tan, Mustapha Abdullahi, Tuo Shi +5
Reinforcement learning (RL) has become a pivotal component of large language model (LLM) post-training, and agentic RL extends this paradigm to operate as agents through multi-turn…
QUASAR: Quantum Assembly Code Generation Using Tool-Augmented LLMs via Agentic RL
Cong Yu, Valter Uotila, Shilong Deng +5
Designing and optimizing task-specific quantum circuits are crucial to leverage the advantage of quantum computing. Recent large language model (LLM)-based quantum circuit generati…
SHARP: Shared State Reduction for Efficient Matching of Sequential Patterns
Cong Yu, Tuo Shi, Matthias Weidlich +1
The detection of sequential patterns in data is a basic functionality of modern data processing systems for complex event processing (CEP), OLAP, and retrieval-augmented generation…