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A decompositional lens is introduced for studying the effects of instruction-tuned models on question answering, mathematics, and code generation in LLM post-training and highlights how the value of code data in post training depends on which categories are combined for which model and task.
Which component matters more depends on the loss assigned to erroneous acceptance: at low liability the planning gain dominates; at high liability the verifier's avoided false passes dominate; and a standalone verifier captures nearly all the false-pass benefit of the full planning-plus-verification stack at a fraction...
The economic value of inference depends on how capacity and task information are distributed across stages of AI production. We study these organizational margins using controlled workflow experiments on externally verified software-engineering tasks. In two matched resource panels, direct execution records the same su...
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