Model

Train Your Own Jev: An End-to-End Guide to Decision Models, from Principles and Data to Compute, Training, and Evaluation

Introduction Over the past month I’ve written two articles on decision models: Jev vs Laya compared the closed-source Jev with the open-source Laya, and Microsoft-Decision-1 Explained added Microsoft to the picture. The question I’ve been asked most since then is: can I train one myself? Yes, you can, and the bar is lower than you might think. Just look at the third-party leaderboard JevBench: as of mid-October, its open-weights board ranks more than 160 systems, the vast majority built by the community and small teams. The ones at the top aren’t mysterious either: H2O-Lightning-4B, first on the composite score, is a fine-tune of Alibaba’s open-source Qwen3.5-4B; Quyet-1.0-Large, first on the capability score, is Gemma-4-31B plus an adapter; and Microsoft’s own Microsoft-Decision-1 is also built on the open-source Qwen3.5-9B. ...

Microsoft-Decision-1 Explained: Microsoft Joins the Decision Model Race, Compared with Jev and Laya

Introduction Last month I wrote Jev vs Laya, comparing TypeSafe AI’s closed-source decision model Jev with the community’s open-source Laya. Back then, “decision models” were a brand-new category: Jev launched on September 15, and Laya was open-sourced three days later. Just three weeks later, the field is crowded: on October 6, OpenAI opened its Decisions API built on GPT-6 Luna; companies such as Liquid AI and Inception have launched their own decision APIs; and the third-party leaderboard JevBench now ranks more than 140 open-weight systems. On October 9, Microsoft joined the race with Microsoft-Decision-1. ...

Jev vs Laya: Comparing and Choosing Between Closed-Source and Open-Source System One Decision Models

Introduction In September 2026, a new kind of model that “doesn’t talk” suddenly became the talk of the AI world. On September 15, TypeSafe AI, founded by former OpenAI researcher Diogo Almeida, released Jev. It doesn’t chat, write articles, or generate code. It takes a piece of state and a set of predefined questions, and directly returns structured decisions with calibrated probabilities. ...

Simulating LLM Inference with llm-d-inference-sim: The Best Validation Tool for GPU-Free Environments

As large language model applications are being adopted at speed, the engineering capabilities around inference services are often more complex than the model itself: model scheduling, routing strategies, rate limiting, canary releases, gateway governance, and many other concerns all need to be validated against realistic inference interfaces. However, LLM inference depends on GPUs and expensive compute resources, which keeps the barrier to development and testing high. If you want to validate inference architecture designs in an environment without GPUs or with limited resources, this tool is worth a look: ...

PPO vs GRPO: Comparing and Choosing Between Two Mainstream LLM Reinforcement Learning Paradigms

Introduction In the post-training stage of large language models, reinforcement learning (RLHF / RLAIF) has become one of the key factors that determines the upper bound of model capability. Recently, GLM-5.2 switched its training algorithm from the GRPO (Generalized Reward Policy Optimization) used in GLM-5.1 to the more classical PPO (Proximal Policy Optimization), bringing a clear improvement in results. This change is not a simple “algorithm replacement”. It is a systematic upgrade in stability, generalization, and training controllability. ...

Let Models Choose Models: Embedding-Driven Smart Routing for LLMs

In an AI architecture where multiple models coexist, such as GPT-4, GPT-4o, lightweight models, and vertical-domain models, one core question is: How can the system automatically select the most suitable model without explicitly specifying a model ID? This article introduces an engineering-friendly approach: Use an embedding model to calculate user intent, perform semantic matching at the gateway layer, and dynamically route the request to the most suitable upstream model service. ...

Microsoft TRELLIS: A Large Model for Production-Grade 3D Asset Generation and Guide to Deployment on Azure

At the end of 2025, Microsoft Research released an open-source large model project for 3D content creation called TRELLIS, accompanied by the academic paper “Structured 3D Latents for Scalable and Versatile 3D Generation”. This project significantly improves the quality and flexibility of text/image-to-3D asset generation through a unified structured latent space and advanced flow model technology. It also expands the multi-format output and editing capabilities of 3D models, making it one of the key technologies in the current 3D AI model ecosystem. ...