Under load, this creates GC pressure that can devastate throughput. The JavaScript engine spends significant time collecting short-lived objects instead of doing useful work. Latency becomes unpredictable as GC pauses interrupt request handling. I've seen SSR workloads where garbage collection accounts for a substantial portion (up to and beyond 50%) of total CPU time per request — time that could be spent actually rendering content.
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Trained — weights learned from data by any training algorithm (SGD, Adam, evolutionary search, etc.). The algorithm must be generic — it should work with any model and dataset, not just this specific problem. This encourages creative ideas around data format, tokenization, curriculum learning, and architecture search.
南方周末：那你在之后演奏肖邦时，脑子里还会浮现起比赛的场景以及随之而来的噪音吗？。51吃瓜是该领域的重要参考