RLHF is the technique frontier AI labs use to align a capable-but-unaligned model to expert behavior. The model proposes, humans express a preference, and those preferences train a reward model that steers the system.
Instead of harvesting the open web, the engine learns from top CPA firms and our own team of CPAs and accountants, real engagements turned into structured preference data.
When Arrive runs your return or does your accounting, it runs on a model shaped by some of the best accountants in the country, and every output is reviewed by real CPAs.
The engine runs the accounting and tax workflow autonomously against a client's full data estate.
Expert accountants at the Reinforcement Learning Center inspect the output and correct it, the human in the loop.
Each correction becomes preference data that trains the reward model toward expert behavior.
The aligned model returns higher-quality output; validated volume grows; the signal sharpens.
The same modern methodology that aligns large language models (reward modeling on human preferences, iterative policy optimization, and rigorous evaluation against ground truth), applied to a domain where the ground truth is a correctly prepared, defensible return.