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Training agents on compressed contexts can lead to significant log-probability gaps, but innovative methods like LogitTree and SDCC offer a robust solution that maintains performance consistency.
Achieving $O(W)$ storage efficiency and high cache hit rates in a large-scale LLM serving system could redefine performance benchmarks for hybrid architectures in production.