How Fast Can Reward Models Score? A Systems Study of C++ and PyTorch Inference Runtimes for RLHF

2026-07-22 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Expert, quick

Summary

A systems study investigated reward model inference runtimes within Reinforcement Learning from Human Feedback (RLHF) pipelines, aiming to alleviate bottlenecks caused by slow scoring. Researchers developed a native C++ inference engine built on ONNX Runtime and compared its performance against PyTorch eager mode, torch.compile, and FastAPI across both CPU and GPU environments. The C++ engine demonstrated superior performance on CPU, outperforming all baselines with non-overlapping confidence intervals. On GPU, however, torch.compile emerged as the fastest option, surpassing the C++ engine, PyTorch, and FastAPI. The study determined that the observed speedup was primarily attributable to ONNX Runtime itself rather than the C++ language. Crucially, the research highlighted that batching strategy significantly impacted performance more than the choice of language or runtime. Correctness was validated, with outputs matching the PyTorch reference to 5.7 x 10^-6 on CPU and 4.2 x 10^-3 on GPU.

Key takeaway

For MLOps Engineers optimizing RLHF training pipelines, your focus should extend beyond rollout generation to reward model scoring efficiency. If you are bottlenecked by scoring, consider implementing ONNX Runtime for CPU-based inference, which significantly outperforms PyTorch eager mode. Crucially, prioritize optimizing your batching strategy, as it yields greater performance gains than runtime or language choices. For GPU workloads, evaluate torch.compile as a strong contender for faster scoring.

Key insights

Optimized reward model inference runtimes and strategic batching are critical to unblock RLHF policy updates.

Principles

• Reward scoring bottlenecks RLHF policy updates.
• Batching strategy is more critical than runtime or language choice.
• ONNX Runtime can provide significant CPU inference speedups.

Method

Developed a native C++ inference engine using ONNX Runtime, then benchmarked it against PyTorch eager mode, torch.compile, and FastAPI on CPU and GPU, validating output correctness.

In practice

• Implement ONNX Runtime for CPU-based reward model inference.
• Optimize batching strategies for RLHF scoring workloads.
• Evaluate torch.compile for GPU-accelerated reward model inference.

Topics

• RLHF
• Reward Models
• Inference Runtimes
• ONNX Runtime
• PyTorch
• Batching Strategy

Best for: AI Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.