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

2026-07-22 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Advanced, quick

Summary

A systems study investigated reward model inference runtimes in Reinforcement Learning from Human Feedback (RLHF) pipelines, comparing a custom C++ inference engine built on ONNX Runtime against PyTorch eager mode, torch.compile, and FastAPI. The research found that the C++ engine decisively outperformed all baselines on CPU, with non-overlapping confidence intervals. However, on GPU, torch.compile emerged as the fastest option, surpassing the C++ engine. The observed speedup was primarily attributed to ONNX Runtime rather than the C++ language itself. A crucial finding was that batching strategy significantly impacted performance, proving more influential than the choice of language or runtime. The study emphasized the importance of repeated, independent runs for reliable performance testing, noting that single runs are insufficient. This optimization aims to unblock policy updates by accelerating reward scoring, freeing up resources for rollout generation.

Key takeaway

For MLOps Engineers optimizing RLHF pipelines, you should critically evaluate your reward model inference setup. If your scoring is CPU-bound, consider migrating to ONNX Runtime for significant speedups, potentially freeing GPU resources for generation. Conversely, for GPU-centric inference, torch.compile offers superior performance. Prioritize optimizing your batching strategy, as it yields greater gains than runtime or language choices.

Key insights

ONNX Runtime and strategic batching are key to accelerating RLHF reward model inference, outperforming PyTorch on CPU.

Principles

• ONNX Runtime boosts CPU inference.
• Batching strategy is paramount for speed.
• Single performance runs are unreliable.

Method

Build a native C++ inference engine on ONNX Runtime, then benchmark against PyTorch eager mode, torch.compile, and FastAPI on both CPU and GPU, using repeated independent runs.

In practice

• Use ONNX Runtime for CPU-bound scoring.
• Optimize batching for reward models.
• Prefer torch.compile for GPU inference.

Topics

• RLHF
• Reward Models
• ONNX Runtime
• PyTorch
• Inference Optimization
• Batching Strategy
• Performance Benchmarking

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 Machine Learning.