Optimizing Reward Model Scoring for Faster RLHF
Summary
A systems study compared C++ and PyTorch inference runtimes for reward models in RLHF pipelines, finding that a native C++ engine on ONNX Runtime significantly outperforms PyTorch on CPU, while batching strategy is crucial for overall speedup.
Why it matters
AI engineers and researchers can significantly accelerate their RLHF training loops by optimizing reward model inference, leading to faster model iteration and deployment.
How to implement this in your domain
- 1Benchmark your current reward model inference runtime on both CPU and GPU.
- 2Consider migrating reward models to a native C++ inference engine with ONNX Runtime for CPU-bound scenarios.
- 3Experiment with different batching strategies for reward model scoring to maximize throughput.
- 4Evaluate `torch.compile` for GPU-bound reward model inference to potentially gain performance.
Who benefits
Key takeaways
- Reward model scoring speed is a critical bottleneck in RLHF pipelines.
- A native C++ engine with ONNX Runtime significantly speeds up CPU inference.
- `torch.compile` can be faster on GPU for reward model inference.
- Batching strategy is more impactful than language or runtime choice for overall speed.
Original post by Venkata Naga Sai Vishnu Rohit Pulipaka, Anish Katta, Deva Rohit Reddy Peddireddy
"arXiv:2607.19712v1 Announce Type: new Abstract: In RLHF pipelines, reward scoring blocks policy updates. Slow scoring bottlenecks the entire loop, since no update runs until every rollout gets a score. And yet most setups just default to PyTorch eager mode or torch.compile, no on…"
View on XOriginally posted by Venkata Naga Sai Vishnu Rohit Pulipaka, Anish Katta, Deva Rohit Reddy Peddireddy on X · view source
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