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MLLMs can be blind to the consequences of their actions, and simply scaling model size won't fix the problem.
Double your LLM inference throughput by routing KV-cache through decoding engines to bypass the bandwidth bottleneck on prefill engines.
By fusing orthogonalized momentum with adaptive noise scaling, NAMO and NAMO-D offer a surprisingly simple recipe for faster and more stable LLM training compared to AdamW and Muon.
