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 is trained on feedback from top CPA firms and accountants, along with our team of CPAs and accountants, turning real engagements into structured preference data. What makes Arrive and ARV unique is how we leverage experts into the human loop. So when ARV runs your return or does your accounting, it runs on a model shaped by some of the best accountants in the country and gets reviewed by real CPAs and accountants.
The Arrive 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. This is the human in the loop.
Each correction becomes preference data that trains the reward model steering the system toward expert behavior.
The aligned model returns higher-quality output; validated volume grows; the signal sharpens. A closed data flywheel.
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.