Since the initial release, community contributions have pushed data efficiency from ~2.4x to 5.5x against modded-nanogpt, more than doubling in a few days. The key changes are: shuffling at the start of each epoch, which had outsized impact on multi-epoch training; learned projections for value embeddings instead of separate embedding tables; swapping squared ReLU for SwiGLU activation; and ensembling multiple models. 10x data efficiency seems reachable in the short term. 100x might be feasible by the end of the year, given how many directions remain unexplored, but it will require serious exploration on the algorithms side.
Eliminates closures entirely by adding extra parameters. No heap allocation for the closure itself. But callers must pass more arguments, and call sites must be updated.
Трамп определил приоритетность Украины для США20:32。下载安装汽水音乐对此有专业解读
Иран назвал путь к прекращению войны14:05，更多细节参见快连下载安装
Tecno's magnetic modular phone introduces some cool concepts, but the execution didn't wow me.
Food waste is instead taken through a process called anaerobic digestion to create biomethane.，详情可参考搜狗输入法2026