Model

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....