A new study investigates the speed of reward scoring in Reinforcement Learning from Human Feedback (RLHF) pipelines, finding that a custom C++ inference engine built on ONNX Runtime significantly outperforms standard PyTorch implementations on CPUs. While the C++ engine also showed gains over PyTorch and FastAPI on GPUs, it was slightly slower than torch.compile. The research highlights that batching strategy is a critical factor in performance, often more so than the choice of language or runtime. AI
IMPACT Optimizing reward model scoring could accelerate RLHF training, potentially leading to faster development of more capable AI agents.
RANK_REASON The cluster contains an academic paper detailing a systems study on inference runtimes for RLHF.
Read on Hugging Face Daily Papers →
- arXiv
- central processing unit
- CPP
- FastAPI
- graphics processing unit
- ONNX Runtime
- PyTorch
- reinforcement learning from human feedback
- torch.compile
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