Beam: Reflection's 501B open-weight model (reflection.ai)

Reflection has unveiled Beam, its first open-weight AI model featuring a staggering 501 billion parameters, of which 23 billion are active. This sophisticated sparse Mixture-of-Experts model is specifically designed for coding, reasoning, and agentic tasks. With significant investments in pretraining using 23.8 trillion tokens from diverse datasets and extensive reinforcement learning (RL) infrastructure, Beam matches or exceeds the capabilities of similar open models while delivering superior inference efficiency. It conducted one of the largest RL training runs to date, utilizing over 10,500 NVIDIA GB300 GPUs and producing more than 100 million rollouts, which enhanced its coding and reasoning performance significantly. The implications of Beam's launch are profound for the AI/ML community. Its architecture emphasizes efficiency, achieving competitive performance in advanced reasoning while consuming 3-4 times less computational resources compared to larger models like Qwen 3.8-Max. Beam’s training framework allows users to adjust reasoning effort based on their compute budgets, catalyzing its adoption in enterprise environments for coding and agentic workloads. Additionally, Beam demonstrates an ability to generalize learned skills across tasks, showcasing a promising direction for future AI systems. The forthcoming release of its weights, technical reports, and developer artifacts will further enrich the open-source ecosystem, granting developers access to cutting-edge AI tools while advancing the pursuit of efficient, high-performing models.