TL;DR:

In our recent work with Professor Michael Graziano (arXiv, thread), we show that adding an auxiliary self-modeling objective to supervised learning tasks yields simpler, more regularized, and more parameter-efficient models.

Across three classification tasks and two modalities, self-modeling consistently reduced complexity (lower RLCT, narrower weight distribution). This restructuring effect may help explain the putative benefits of self-models in both ML and biological systems.

Agents who self-model may be reparameterized to better predict themselves, predict others, and be predicted by others. Accordingly, we believe that further exploring the potential effects of self-modeling on cooperation emerges as a promising neglected approach to alignment. This approach may also exhibit a 'negative alignment tax' to the degree that it may end up enhancing alignment and rendering systems more globally effective.

Introduction

In this post, we discuss some of the core findings and implications of our recent paper, Unexpected Benefits of Self-Modeling in [...]

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Outline:

(00:07) TL;DR:

(01:06) Introduction

(03:06) Implementing self-modeling across diverse classification tasks

(04:08) How we measured network complexity

(05:19) Key result

(06:03) Relevance of self-modeling to alignment

(07:07) Challenges, considerations, and next steps

(08:50) Appendix: Interpreting Experimental Outcomes

(09:07) Does the Network Simply Learn the Identity Function?

(10:25) How Does Self-Modeling Differ from Traditional Regularization?

The original text contained 1 footnote which was omitted from this narration.

The original text contained 3 images which were described by AI.

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First published:
October 23rd, 2024

Source:
https://www.lesswrong.com/posts/5se67gAcaExEYdCCg/self-prediction-acts-as-an-emergent-regularizer

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Narrated by TYPE III AUDIO.

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“Self-prediction acts as an emergent regularizer” by Cameron Berg, Judd Rosenblatt, Mike Vaiana, Diogo de Lucena, florin_pop, AE Studio

LessWrong (30+ Karma)

“Self-prediction acts as an emergent regularizer” by Cameron Berg, Judd Rosenblatt, Mike Vaiana, Diogo de Lucena, florin_pop, AE Studio