Pre-training was conducted in three phases, covering long-horizon pre-training, mid-training, and a long-context extension phase. We used sigmoid-based routing scores rather than traditional softmax gating, which improves expert load balancing and reduces routing collapse during training. An expert-bias term stabilizes routing dynamics and encourages more uniform expert utilization across training steps. We observed that the 105B model achieved benchmark superiority over the 30B remarkably early in training, suggesting efficient scaling behavior.
场景：这就好比是一个空荡荡的剧院舞台。我们把它涂成了深灰色 (0x222222)，方便看清演员。
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