New C++ engine speeds up RLHF reward scoring on CPUs

Researchers have developed a new C++ inference engine using ONNX Runtime for reward model scoring in Reinforcement Learning from Human Feedback (RLHF) pipelines. This engine was benchmarked against PyTorch eager mode, torch.compile, and FastAPI on both CPU and GPU. The C++ engine demonstrated superior performance on CPUs, outperforming all baselines, while on GPUs, torch.compile achieved faster results. The study also highlighted that batching strategy, particularly length-aware bucketing, significantly impacts throughput, especially on GPUs. AI

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

RANK_REASON The cluster contains a research paper detailing a systems study of inference runtimes for RLHF. [lever_c_demoted from research: ic=1 ai=1.0]

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• central processing unit
• CPP
• FastAPI
• graphics processing unit
• ONNX Runtime
• PyTorch
• reinforcement learning from human feedback
• torch.compile

• paper
• infra

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