Reinforcement Learning with Verifiable Rewards
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TLDRocket
16 October 2026
Agentic coding models keep shipping—and the day’s biggest thread was Reflection AI publishing Beam as open weights, complete with post-training recipes and customization tooling. Beam is built for long-horizon software work with 501 billion total parameters and a 23 billion active slice per forward pass, trained over two months on several thousand high-end GPUs. The practical part is what matters: deploy-ready serving configs, quantized variants, and AWS options via SageMaker JumpStart or Kubernetes-native SageMaker HyperPod Inference, with checkpoints in BF16, FP8, or NVFP4.
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