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The Depth of Gradient Propagation Determines the Essence of Learning

The Depth of Gradient Propagation Determines the Essence of Learning

@vista8

梯度是一个信号，告诉你：你错了，往哪个方向改，改多少。

讲了一个概念，别人没听懂。

这个"没听懂"就是loss，就是输出和真正理解之间的差距。

问题到底出在哪一层？

是比喻不好？还是对概念拆解是错的？还是根上就没搞清楚本质？

回传就是上面追问的过程，从结果一路往回查。

这就是梯度回传到了底层。

大模型也是这样，一层一层往回问：这个错，是哪一层的参数贡献的？

每一层分到一个梯度值，该为这个错误负多少责任，该往哪个方向调。

梯度告诉每一层该怎么改，回传决定这个信号能传多深。

传得越深，改得越底层，学得越本质。Show More

Mar 20, 2026, 11:31 PM View on X

0 Replies

1 Retweets

12 Likes

2,576 Views  向阳乔木 @vista8

One Sentence Summary

Using the concept of gradient to explain the learning feedback mechanism—the deeper the gradient propagates, the more essential the learning.

Summary

As a deeper expansion of the previous tweet, the author explains the relationship between gradient and learning in detail. Gradient is a signal telling you: you're wrong, which direction to change, and by how much. If you explain a concept and others don't understand, that 'not understanding' is the loss—the gap between output and true comprehension. Where exactly is the problem? Is the metaphor poor? Or is the concept breakdown wrong? Or was the essence fundamentally misunderstood? Backpropagation is the process of probing upward, tracing back from the result layer by layer. It's the same with LLMs—asking layer by layer which layer's parameters contributed to this error. Each layer receives a gradient value, indicating how much responsibility it bears for this error and which direction to adjust. Gradient tells each layer how to modify, and backpropagation determines how far this signal can propagate. The deeper it propagates, the more foundational the changes, the more essential the learning.

AI Score

87

Influence Score 2

Published At Yesterday

Language

Chinese

Tags

Gradient Descent

Backpropagation

Deep Learning

Learning Feedback

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The Depth of Gradient Propagation Determines the Essence ...