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.
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View a PDF of the paper titled 130k Lines of Formal Topology in Two Weeks: Simple and Cheap Autoformalization for Everyone?, by Josef Urban
开超跑通常是一件体力活，运动化的座舱通常很难让人放松下来。为了追求极致的路感反馈。性能车里塞满的总是非常狭窄且僵硬的硬核座椅。
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