4 citations · 8 across the 6 of their papers we have counts for
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cs.LG2026
IsoCompute Playbook: Optimally Scaling Sampling Compute for LLM RL
Zhoujun Cheng, Yutao Xie, Yuxiao Qu +12
While scaling laws guide compute allocation for LLM pre-training, analogous prescriptions for reinforcement learning (RL) post-training of large language models (LLMs) remain poorl…
cs.LG2023★ 4 cited
Cappy: Outperforming and Boosting Large Multi-Task LMs with a Small Scorer
Bowen Tan, Yun Zhu, Lijuan Liu +3
Large language models (LLMs) such as T0, FLAN, and OPT-IML, excel in multi-tasking under a unified instruction-following paradigm, where they also exhibit remarkable generalization…
cs.LG2023
RedCoast: A Lightweight Tool to Automate Distributed Training of LLMs on Any GPU/TPUs
Bowen Tan, Yun Zhu, Lijuan Liu +5
The recent progress of AI can be largely attributed to large language models (LLMs). However, their escalating memory requirements introduce challenges for machine learning (ML) re…