Principle:NVIDIA NeMo Aligner Reward Model Serving
| Principle Metadata | |
|---|---|
| Type | Principle |
| Domains | Serving, Distributed_Systems |
| Last Updated | 2026-02-07 00:00 GMT |
| Related Implementation | Implementation:NVIDIA_NeMo_Aligner_RewardModelServer_Run |
Overview
Deployment pattern for exposing a trained reward model as an HTTP inference service for use by RLHF actor processes.
Description
In the multi-process RLHF architecture, the reward model runs as a separate process from the actor. The reward model serving principle wraps a frozen reward model behind a PyTriton HTTP server that accepts batches of generated text (as tokens or strings) and returns scalar reward scores.
The server handles:
- Dynamic batching — Groups multiple requests to amortize communication overhead
- Distributed inference — Coordinates across model-parallel ranks for large models
- Thread-safe request processing — Ensures safe concurrent access from multiple actor processes
Non-rank-0 processes run a subscriber loop that receives broadcast signals to execute synchronized distributed inference.
Usage
Use when deploying a trained reward model for PPO or REINFORCE training. The server runs as a separate process (serve_reward_model.py) and is accessed by actor processes via HTTP clients (RemoteGPTRMClient). Required for any RLHF pipeline that separates reward computation from policy training.
Architecture overview:
+----------------+ HTTP +---------------------+
| Actor Process | ----------------> | Reward Model Server |
| (PPO/REINFORCE)| (PyTriton) | (serve_reward_model.py) |
+----------------+ +---------------------+
| Rank 0 (server) |
| Rank 1..N (subscribers) |
+---------------------+
Key configuration:
- The reward model server is launched as an independent process
- Actor processes connect via RemoteGPTRMClient using the server's HTTP endpoint
- Dynamic batching is configured on the PyTriton server side
Theoretical Basis
The design follows a client-server architecture for distributed RLHF. The server broadcasts computation signals from rank 0 to all model-parallel ranks, ensuring synchronized distributed inference across GPUs.
Key architectural properties:
- Separation of concerns — Reward computation is decoupled from policy optimization
- Broadcast synchronization — Rank 0 receives HTTP requests and broadcasts to all ranks for distributed forward passes
- Dynamic batching — Amortizes communication overhead by grouping multiple requests into a single forward pass