AWS Machine Learning Blog· Clement Perrot·· 7 h ago
Introducing Reflection Beam: an open-weight model for agentic coding on AWS
Introducing Reflection Beam: an open-weight model for agentic coding on AWS
AI summary
Reflection AI's Beam is an open-weight model for agentic coding and long-horizon tasks. This post shows how to deploy Beam on AWS: start with an Amazon SageMaker JumpStart real-time endpoint, or use Amazon SageMaker HyperPod Inference with JumpStart, SGLang, or vLLM for Kubernetes-native control over distributed GPU infrastructure.
Selection record
Threshold 60Official, first-handFirst 71Second 62
AdmittedSum of both 133 ≥ twice the threshold 120
- Source tier
- Official, first-hand; this tier's threshold is 60
- Pre-filter
- passed：介绍开源权重模型Beam及AWS部署，属AI技术
- Why it was chosen
- Reflection AI open-sources Beam's weights and three precision versions, and lays out a deployment path on AWS from managed endpoint to self-hosted runtime.
- Same event
A model scores each item twice, independently, against one written standard, out of 100. An item is admitted only when the two scores add up to twice the threshold. The threshold is set per source tier.
Source: AWS Machine Learning Blog · aws.amazon.com