Post-training case study: Reaching frontier-level intelligence on global payroll with Deel

AI21’s Forward-Deployed Researchers and Deel’s Emet AI lab post-trained NVIDIA Nemotron 3.5 Super VL for global payroll support, a domain requiring accurate, complete, locally compliant answers across changing laws, contracts, and company policies. Using Deel’s EmBench Payroll benchmark, which evaluates factual, coverage, and stylistic criteria, the team found that the base model often possessed relevant knowledge but produced incomplete or overly cautious answers. They first applied near-policy supervised fine-tuning (SFT) to 412 high-quality, hint-guided outputs generated by the model itself, then used self-distillation policy optimization (SDPO) with privileged guidance to reinforce desired answer characteristics through fresh on-policy rollouts. The combined approach increased the model’s criteria-met score from 51.3% to 72.0% and its fully correct-answer rate from 10.0% to 33.9%, surpassing Claude Opus 5.5 by roughly eight points on the reported quality measure while costing about 50 times less per million tokens.

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