2 citations · 2 across the 2 of their papers we have counts for
3 papers
cs.LG2026
Diagnosing Training Inference Mismatch in LLM Reinforcement Learning
Tianle Zhong, Neiwen Ling, Yifan Pi +5
Modern LLM RL systems separate rollout generation from policy optimization. These two stages are expected to produce token probabilities that match exactly. However, implementation…
cs.DB2023
RINAS: Training with Dataset Shuffling Can Be General and Fast
Tianle Zhong, Jiechen Zhao, Xindi Guo +2
Deep learning datasets are expanding at an unprecedented pace, creating new challenges for data processing in model training pipelines. A crucial aspect of these pipelines is datas…
cs.DC2023★ 2 cited
RTP: Rethinking Tensor Parallelism with Memory Deduplication
Cheng Luo, Tianle Zhong, Geoffrey Fox
In the evolving landscape of neural network models, one prominent challenge stand out: the significant memory overheads associated with training expansive models. Addressing this c…