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Achieving substantial reductions in training latency, AC$^2$P$^2$SL redefines the efficiency of split learning in edge networks without compromising data privacy.
You can get surprisingly reliable multi-turn LLM agents by having a small, open-source critic supervise a powerful, fixed LLM actor *during* the conversation.
