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KV-Invariant Transformer Expansion (KITE) is introduced, a scaling paradigm that trains the model from a smaller size to a larger size (i.e., saving training costs via upcycling), while places newly added parameters in regions that do not affect attention KV.
This work introduces Farseer, a novel and refined scaling law offering enhanced predictive accuracy across scales, and provides new insights into optimal compute allocation, better reflecting the nuanced demands of modern LLM training.
MISA-T, a routing-layer admission policy for mixed rollout serving that combines adaptive session admission, workload-aware KV-capacity allocation, and residency-time-aware KV accounting, is presented.
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