Unverified50% confidenceFactExact time
Generative decision models must process the current context together with all candidate options on every call, with the computational load increasing as the number of options grows.
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Long-term
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10/4/2026
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UnverifiedThe output of a decision model is often a label or probability distribution over a finite action set, similar to a policy network in reinforcement learning, whereas generative models require autoregressive sampling over a large vocabulary.77% similarUnverifiedDecision models focus on answering yes/no judgments, multiple-choice questions, and probability-scored rating-type problems.77% similarUnverifiedBuilding an inference engine tailored for a specific model (model specialization) enables deeper optimization in operator implementation, memory layout, and quantization strategy, whereas llama.cpp adopts a general-support strategy aimed at compatibility with multiple model architectures76% similarUnverifiedEach time a token is generated via a forward pass, the model must go through a complete multi-head attention computation and feed-forward network layers, with the amount of computation determined by the model's parameter scale76% similarUnverifiedAutoregressive model inference must be executed strictly in serial; generating each token requires one complete forward pass, which is an important structural reason for the high cost of large model inference.76% similar
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