In this post, we walk you through Pushpay’s journey in building this solution and explore how Pushpay used Amazon Bedrock to create a custom generative AI evaluation framework for continuous quality assurance and establishing rapid iteration feedback loops on AWS.
Large language models excel with reinforcement learning (RL), but fully unlocking this potential requires a mid-training stage. An effective mid-training phase should identify a compact set of useful actions and enable fast selection among them through online RL. We formalize this intuition by prese
This blog post describes how Totogi automates change request processing by partnering with the AWS Generative AI Innovation Center and using the rapid innovation capabilities of Amazon Bedrock.
Diffusion large language models (dLLMs) are compelling alternatives to autoregressive (AR) models because their denoising models operate over the entire sequence. The global planning and iterative refinement features of dLLMs are particularly useful for code generation. However, current training and
“MechStyle” allows users to personalize 3D models, while ensuring they’re physically viable after fabrication, producing unique personal items and assistive technology.
