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

一句话概要

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.

原文开头节选

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 ).

(以上为原文节选,完整内容见下方”原文来源”)

这条动态今日登上 Hacker News 首页(972 赞 / 229 评论,来源 laya.convaiinnovations.com)。技术雷达每日自动聚合 AI 工程、后端架构、DevOps 方向的前沿动态;相关工程落地可浏览下方的相关服务与延伸阅读,或直接与我们团队交流。