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Omni-modal foundation models achieve self-improvement via RL compute scaled to 1M context, leveraging asynchronous training and agentic grading for enhanced intelligence.
Training on the entire deployed matrix can unlock up to 81.4% of a model's capacity that was previously unreachable, leading to unprecedented performance gains in low-rank distillation.
PhyAI achieves up to 4.65x speedup in Physical AI tasks by unifying disparate inference processes into a single, efficient runtime.
