本文探讨了LLC（损失景观曲率）在AI模型中的表现，特别是在包含LayerNorm和损失景观中存在剧烈变化的玩具模型中。研究通过DLNS笔记本和相关代码进行，结果显示LLC的行为符合预期：损失的急剧下降伴随着LLC的急剧上升。研究旨在验证LLC在特定情况下的有效性，并观察LayerNorm模型损失景观的特性。尽管未发现重大见解，但研究结果对AI安全和可解释性领域具有一定的参考价值。

🧐研究背景：该研究是作者为了提升在AI安全和可解释性领域的研究经验而进行的个人项目，主要基于已有的代码。

💡研究方法：研究基于devinterp库的DLNS笔记本以及提供的示例代码进行，通过分析LLC在包含LayerNorm和损失景观剧烈变化的玩具模型中的表现。

📈研究结果：LLC的行为符合预期，损失的大幅下降伴随着LLC的大幅上升，表明损失景观具有高度的分割性。

🤔研究讨论：该项目旨在验证LLC在具有剧烈变化损失景观中的有效性，并观察LayerNorm模型损失景观的特性。结果表明LLC的行为符合预期，确认了其反映网络训练过程基本属性的能力。

Published on March 20, 2025 4:18 PM GMT

Epistemic status: small exploration without previous predictions, results low-stakes and likely correct.

Introduction

As a personal exercise for building research taste and experience in the domain of AI safety and specifically interpretability, I have done four minor projects, all building upon code previously written. They were done without previously formulated hypotheses or expectations, but merely to check for anything interesting in low-hanging fruit. In the end, they have not given major insights, but I hope they will be of small use and interest for people working in these domains.

This is the fourth project: checking how the LLC behaves for toy models containing LayerNorm and sharp transitions in the loss landscape (this project from Timaeus).

TL;DR results

LLC behaves as expected. Large drops in loss are mirrored by large spikes in the LLC.

Methods

The basis for these findings is the DLNS notebook of the devinterp library, plus the example code given. Notebooks and graphs can be found here.

Results

There does not seem to be anything peculiar about the progression of the LLC. The sudden drop in loss was mirrored by a sudden rise in LLC, showing a highly compartmentalized loss landscape.

Discussion

This was an attempt to check the validity of LLC for sharply delineated loss landscapes, and to see if any strange results appear when looking at the loss landscape of a LayerNorm model, which contain a feature that the interpretability community dislikes for many reasons. LLC behaved as expected.

I must mention that the project page loss does not one-to-one correspond to the losses registered on the given model. This might be a problem on my end, but I doubt it is a large issue.

Conclusion

This confirmation that LLC acts as it is supposed to even in strange loss regimes is encouraging, indicating that it truly reflects fundamental properties of the network training process.

Acknowledgements

I would like to thank the original Timaeus team for starting this research direction, establishing its methods, and writing the relevant code.

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