InfraRSI harness applies recursive self-improvement to LLM training
On four dense and MoE workloads, the authors report up to 2.75x speedup and mean MFU rising from 17.4% to 30.0%.
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On four dense and MoE workloads, the authors report up to 2.75x speedup and mean MFU rising from 17.4% to 30.0%.
Four scheduling techniques bound all memory peaks in long-context MoE training, enabling 120B-667B models at 1M-token context with up to 10.4x throughput.
Radix Ark open-sources Miles v0.1, a full-stack RL post-training framework demonstrated with asynchronous agentic RL on GLM-5.2 744B-A40B across 64 GB300 GPUs.
Intuition Machines CEO proposes data diodes and one-way networks to physically prevent frontier AI models from escaping training sandboxes, citing the OpenAI Hugging Face incident.
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