Principle:CarperAI Trlx Reward Model Serving
| Knowledge Sources | |
|---|---|
| Domains | Reward_Modeling, Inference, Deployment |
| Last Updated | 2026-02-07 16:00 GMT |
Overview
A deployment principle for wrapping a trained reward model into a callable reward function for use during PPO training.
Description
After a reward model is trained on pairwise comparisons, it must be wrapped in a function that conforms to trlx's reward function interface for use during PPO training. This involves loading the model weights, setting up inference (half-precision, separate GPU, batched scoring), and optionally computing delta rewards (generated score minus reference score).
The serving pattern in trlx supports two backends: local PyTorch inference (using a lightweight RewardModel class on a dedicated GPU) and remote NVIDIA Triton Inference Server (for production-scale serving with batched GPU inference). The choice is made automatically based on the TRITON_HOST environment variable.
Usage
Use reward model serving when connecting a trained reward model to PPO training. The create_reward_fn() factory function handles model loading, device placement, and wrapping in the trlx reward function interface. The resulting function is passed to trlx.train(reward_fn=...).
Theoretical Basis
Reward model serving bridges training and deployment:
Pseudo-code:
# Abstract reward serving pattern (not real implementation)
def create_reward_fn():
# Load trained reward model
reward_model = load_reward_model(checkpoint_path)
reward_model.eval().half().to(device)
def reward_fn(samples, prompts, **kwargs):
# Score generated text
rewards = reward_model(tokenize(samples))
# Optionally compute delta reward
if delta_reward:
original_rewards = reward_model(tokenize(references))
return rewards - original_rewards
return rewards
return reward_fn
Key design choices:
- Device placement: Reward model on a separate GPU to avoid memory contention with the policy model
- Half-precision: fp16 inference for 2x memory reduction with negligible quality loss
- Batched scoring: Process samples in mini-batches to fit in GPU memory
- Delta rewards: Compute improvement over reference to reduce reward scale variance