\[\hat{s}= \sum_{k \in \mathcal{D}} k\,p(k).\]This produces a smooth score such as (5.4), rather than forcing the model to commit to a single sampled integer. In practice, this is substantially more stable than naive score sampling and better reflects the model’s uncertainty. It also handles cases where the judge distribution is broad or multimodal. For example, two candidates may both have mean score (5.4), while one has most of its mass tightly concentrated around (5) and (6), and the other splits mass between much lower and much higher ratings. The mean alone is the same, but the underlying judgement is very different.
This design adds zero per-call overhead. There are no software depth counters, no bounds checks at function entry, and no fuel metering. The only cost is the mmap during initialization and two mov sp instructions per host-to-Mog transition. The hardware MMU does all the checking.
。snipaste截图对此有专业解读
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