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but they are deprecated in favor of callback protocols.
，这一点在体育直播中也有详细论述
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.
My up-to-date AGENTS.md file for Python is available here, and throughout my time working with Opus, it adheres to every rule despite the file’s length, and in the instances where I accidentally query an agent without having an AGENTS.md, it’s very evident. It would not surprise me if the file is the main differentiator between those getting good and bad results with agents, although success is often mixed.