4 papers
Accelerating Long-Tail Generation in Synchronous RLHF Training via Adaptive Tensor Parallelism
Long Zhao, Qinghe Wang, Jiaan Zhu +5
Reinforcement Learning from Human Feedback (RLHF) has become a key post-training paradigm for improving model quality. However, the synchronous three-stage RLHF pipeline is often b…
AuroraRL: Fast, Fault-Tolerant, and Cost-Efficient Reinforcement Learning over Decentralized Network
Chaoyi Ruan, Geng Luo, Xinyi Wan +12
LLM reinforcement learning (RL) requires frequent synchronization of large model parameters between the trainer and distributed rollout actors. High-throughput RL post-training the…
SmallThinker: A Family of Efficient Large Language Models Natively Trained for Local Deployment
Yixin Song, Zhenliang Xue, Dongliang Wei +11
While frontier large language models (LLMs) continue to push capability boundaries, their deployment remains confined to GPU-powered cloud infrastructure. We challenge this paradig…
TeleEval-OS: Performance evaluations of large language models for operations scheduling
Yanyan Wang, Yingying Wang, Junli Liang +10
The rapid advancement of large language models (LLMs) has significantly propelled progress in artificial intelligence, demonstrating substantial application potential across multip…