Reflection AI Unveils Beam, a 501B Open-Weight Mixture-of-Experts Model for Coding and Agents
- Architecture Design: Beam utilizes interleaved local and global attention with fine-grained routed experts, maintaining stable residual norms across all 52 layers using depth-based scaling and SandwichNorm.
- Pretraining Scale: The model was pretrained on 23.8 trillion tokens from diverse sources in under four weeks utilizing 6,144 NVIDIA GB300 NVL72 GPUs, achieving a 92.3% goodput near completion.
- Reinforcement Learning: Reflection utilized 10,500 NVIDIA GB300 GPUs for four weeks of high-compute reinforcement learning, generating over 100 million rollouts across 1.3 billion sandboxes.
- Deployment Status: Beam is currently undergoing final red-teaming and is not yet available for self-hosting, though developers can join an early access waitlist via the Reflection platform.
The Context & Background
The release represents Reflection AI's effort to advance the Western open-weight frontier by focusing heavily on inference efficiency and adjustable reasoning effort parameters. Developed to match performance against larger established models, Beam addresses the growing enterprise demand for capable, compute-efficient coding and autonomous agent infrastructure.
Why it matters:Beam's focus on high inference efficiency and adjustable reasoning parameters offers enterprises a powerful, cost-effective tool for complex coding and autonomous agent tasks. By achieving high performance while consuming significantly less inference compute, it sets a new benchmark for scalable open-weight artificial intelligence models.
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