I built non-autoregressive decision models with RL a year ago

In one line

Non-autoregressive decision model over 100+ languages in a single forward pass (~33ms on GPU). Built with RLCD and proper scoring rules by Convai Innovations.

Opening excerpt

From our March 2025 arXiv paper on sequence conversion trajectories to Laya: a sub-35ms open-weight System 1 decision engine with RLCD, multilingual routing across 100+ languages, and state-of-the-art calibration.

Figure 1: Full benchmark board — accuracy on shared datasets, 9 application workflows, 51-language sweep, T4 latency, and calibration repair.

Everyone in AI right now is talking about a new kind of model: an architecture that is not autoregressive, does not generate text, and gives lightning-fast probability predictions over structured schemas.

I worked on this literally one year back in March 2025. I spent months of hard work, sweat, and sleepless nights building it, published an arXiv paper ( arXiv:2503.23303 ), released the model weights on Hugging Face ( sales-conversion-model-reinf-learning ), published the open dataset ( saas-sales-conversations ), built a PyPI package, and posted the whole approach on Reddit ( r/LocalLLaMA discussion ).

(Excerpted from the original; full article via the source link below.)

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