Reflection AI has introduced Beam, a 501-billion-parameter open-weight model designed for reasoning, coding, and agentic tasks. Trained on nearly 24 trillion tokens, Beam claims to match Chinese models' performance while requiring significantly less inference compute. The company positions Beam as a cost-effective alternative to both Western and Chinese AI models, targeting enterprise and public sector applications.
techcrunch.com
· 2026-10-05
Reflection has unveiled Beam, its first open-weight model with 501 billion parameters, designed for tasks like coding, reasoning, and agentic workloads. The model was pretrained on 23.8 trillion tokens and refined through extensive reinforcement learning using over 100 million rollouts on NVIDIA GPUs, aiming to achieve high efficiency and performance. Beam is currently undergoing final testing before a public release of its weights and technical documentation later this month.
reflection.ai
· 2026-10-05
A 2017 explainer describes impedance matching, the engineering technique of adding a component to help a system absorb energy more efficiently rather than reflecting it. It illustrates the concept with examples ranging from car transmissions and electrical transformers to sloped beaches and anechoic foam spikes.
edge.org
· 2026-09-07
GoKawiil