New C++ engine boosts RLHF reward scoring speed on CPUs

A new study published on arXiv investigates the performance of reward model scoring in reinforcement learning from human feedback (RLHF) pipelines. Researchers developed a C++ inference engine using ONNX Runtime, finding it significantly faster than PyTorch eager mode and FastAPI on CPUs. While the C++ engine also outperformed PyTorch and FastAPI on GPUs, it was slightly slower than another unspecified runtime. The study highlights that batching strategy has a greater impact on performance than language or runtime choice. AI

IMPACT Optimizing reward model scoring could accelerate RLHF training, potentially leading to faster development of more capable AI models.

RANK_REASON Academic paper detailing a systems study on inference runtimes for RLHF. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• arXiv
• central processing unit
• CPP
• FastAPI
• graphics processing unit
• ONNX Runtime
• PyTorch
• reinforcement learning from human feedback

• paper
• infra

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