In­ner Alignment

TagLast edit: 9 Oct 2023 23:35 UTC by Linda Linsefors

Inner alignment asks the question: How can we robustly aim our AI optimizers at any objective function at all?

More specifically, Inner Alignment is the problem of ensuring mesa-optimizers (i.e. when a trained ML system is itself an optimizer) are aligned with the objective function of the training process.

As an example, evolution is an optimization force that itself ‘designed’ optimizers (humans) to achieve its goals. However, humans do not primarily maximize reproductive success, they instead use birth control while still attaining the pleasure that evolution meant as a reward for attempts at reproduction. This is a failure of inner alignment.

The term was first given a definition in the Hubinger et al paper Risk from Learned Optimization:

We refer to this problem of aligning mesa-optimizers with the base objective as the inner alignment problem. This is distinct from the outer alignment problem, which is the traditional problem of ensuring that the base objective captures the intended goal of the programmers.

Goal misgeneralization due to distribution shift is another example of an inner alignment failure. It is when the mesa-objective appears to pursue the base objective during training but does not pursue it during deployment. We mistakenly think that good performance on the training distribution means that the mesa-optimizer is pursuing the base objective. However, this might have occurred only because there were some correlations in the training distribution resulting in good performance on both the base and mesa objectives. When we had a distribution shift from training to deployment it caused the correlation to be broken and the mesa-objective failed to generalize. This is especially problematic when the capabilities successfully generalize to the deployment distribution while the objectives/​goals don’t. Since now we have a capable system that is optimizing for a misaligned goal.

To solve the inner alignment problem, some sub-problems that we would have to make progress on include things such as deceptive alignment, distribution shifts, and gradient hacking.

Inner Alignment Vs. Outer Alignment

Inner alignment is often talked about as being separate from outer alignment. The former deals with working on guaranteeing that we are robustly aiming at something, and the latter deals with the problem of what exactly are we aiming at. For more information see the corresponding tag.

It should be kept in mind that you can have both inner and outer alignment failures together. It is not a dichotomy and often even experienced alignment researchers are unable to tell them apart. This indicates that the classifications of failures according to these terms are fuzzy. Ideally, we don’t think of a binary dichotomy of inner and outer alignment that can be tackled individually but of a more holistic alignment picture that includes the interplay between both inner and outer alignment approaches.

Related Pages:

Mesa-Optimization, Treacherous Turn, Eliciting Latent Knowledge, Deceptive Alignment, Deception

External Links:

The In­ner Align­ment Problem

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103 points
17 comments13 min readLW link

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168 points
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355 points
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85 points
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70 points
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63 points
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62 points
18 comments23 min readLW link

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80 points
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27 points
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95 points
57 comments16 min readLW link

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21 points
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21 points
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49 comments18 min readLW link

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138 points
22 comments47 min readLW link3 reviews

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26 points
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11 comments4 min readLW link

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102 points
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13 points
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60 points
14 comments2 min readLW link

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41 points
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22 points
10 comments21 min readLW link

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33 points
4 comments4 min readLW link

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26 points
7 comments11 min readLW link

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26 points
1 comment15 min readLW link

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4 Apr 2022 12:59 UTC
71 points
20 comments16 min readLW link

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16 points
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12 points
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169 points
14 comments7 min readLW link

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6 points
7 comments1 min readLW link

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41 points
45 comments4 min readLW link

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58 points
8 comments20 min readLW link

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12 points
1 comment3 min readLW link

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2 comments1 min readLW link

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126 points
23 comments6 min readLW link

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38 points
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45 points
23 comments4 min readLW link

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11 points
0 comments5 min readLW link

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42 points
6 comments10 min readLW link

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11 points
10 comments2 min readLW link

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13 points
2 comments3 min readLW link

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9 points
0 comments3 min readLW link

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37 points
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37 points
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41 points
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57 points
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27 points
4 comments6 min readLW link

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14 points
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2 points
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63 points
72 comments44 min readLW link1 review

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53 points
8 comments2 min readLW link

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20 points
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18 points
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36 points
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127 points
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74 points
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8 points
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34 points
20 comments34 min readLW link

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13 points
0 comments13 min readLW link

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19 points
5 comments7 min readLW link

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85 points
6 comments18 min readLW link

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Thane Ruthenis28 Dec 2022 18:28 UTC
23 points
38 comments3 min readLW link

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19 points
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18 Jan 2023 20:09 UTC
54 points
16 comments13 min readLW link

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160 points
21 comments5 min readLW link

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23 points
0 comments3 min readLW link

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6 points
0 comments2 min readLW link

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1 point
7 comments1 min readLW link

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78 points
7 comments7 min readLW link

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6 points
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22 points
4 comments39 min readLW link

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148 points
59 comments57 min readLW link

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6 points
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4 points
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3 points
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16 points
0 comments8 min readLW link

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1 point
1 comment3 min readLW link

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1 point
0 comments8 min readLW link

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15 points
0 comments26 min readLW link

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31 points
19 comments1 min readLW link

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22 points
15 comments1 min readLW link

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9 points
7 comments12 min readLW link

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2 points
7 comments3 min readLW link

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11 points
6 comments2 min readLW link

Archety­pal Trans­fer Learn­ing: a Pro­posed Align­ment Solu­tion that solves the In­ner & Outer Align­ment Prob­lem while adding Cor­rigible Traits to GPT-2-medium

MiguelDev26 Apr 2023 1:37 UTC
14 points
5 comments10 min readLW link

AI Align­ment: A Com­pre­hen­sive Survey

Stephen McAleer1 Nov 2023 17:35 UTC
14 points
0 comments1 min readLW link

​​ Open-ended/​Phenom­e­nal ​Ethics ​(TLDR)

Ryo 9 Nov 2023 16:58 UTC
3 points
0 comments1 min readLW link

Op­tion­al­ity ap­proach to ethics

Ryo 13 Nov 2023 15:23 UTC
7 points
2 comments3 min readLW link

Is In­ter­pretabil­ity All We Need?

RogerDearnaley14 Nov 2023 5:31 UTC
1 point
0 comments1 min readLW link

Why small phe­nomenons are rele­vant to moral­ity ​

Ryo 13 Nov 2023 15:25 UTC
1 point
0 comments3 min readLW link

Re­ac­tion to “Em­pow­er­ment is (al­most) All We Need” : an open-ended alternative

Ryo 25 Nov 2023 15:35 UTC
9 points
3 comments5 min readLW link

Align­ment is Hard: An Un­com­putable Align­ment Problem

Alexander Bistagne19 Nov 2023 19:38 UTC
−5 points
4 comments1 min readLW link

In­tro­duc­tion and cur­rent re­search agenda

quila20 Nov 2023 12:42 UTC
27 points
1 comment1 min readLW link

Colour ver­sus Shape Goal Mis­gen­er­al­iza­tion in Re­in­force­ment Learn­ing: A Case Study

Karolis Ramanauskas8 Dec 2023 13:18 UTC
13 points
1 comment4 min readLW link

Tak­ing Into Ac­count Sen­tient Non-Hu­mans in AI Am­bi­tious Value Learn­ing: Sen­tien­tist Co­her­ent Ex­trap­o­lated Volition

Adrià Moret2 Dec 2023 14:07 UTC
26 points
31 comments42 min readLW link

Lan­guage Model Me­moriza­tion, Copy­right Law, and Con­di­tional Pre­train­ing Alignment

RogerDearnaley7 Dec 2023 6:14 UTC
3 points
0 comments11 min readLW link

A sim­ple en­vi­ron­ment for show­ing mesa misalignment

Matthew Barnett26 Sep 2019 4:44 UTC
71 points
9 comments2 min readLW link

Ba­bies and Bun­nies: A Cau­tion About Evo-Psych

Alicorn22 Feb 2010 1:53 UTC
81 points
843 comments2 min readLW link

2-D Robustness

Vlad Mikulik30 Aug 2019 20:27 UTC
85 points
8 comments2 min readLW link

Try­ing to mea­sure AI de­cep­tion ca­pa­bil­ities us­ing tem­po­rary simu­la­tion fine-tuning

alenoach4 May 2023 17:59 UTC
4 points
0 comments7 min readLW link

My preferred fram­ings for re­ward mis­speci­fi­ca­tion and goal misgeneralisation

Yi-Yang6 May 2023 4:48 UTC
24 points
1 comment8 min readLW link

Is “red” for GPT-4 the same as “red” for you?

Yusuke Hayashi6 May 2023 17:55 UTC
9 points
6 comments2 min readLW link

Re­ward is the op­ti­miza­tion tar­get (of ca­pa­bil­ities re­searchers)

Max H15 May 2023 3:22 UTC
32 points
4 comments5 min readLW link

Sim­ple ex­per­i­ments with de­cep­tive alignment

Andreas_Moe15 May 2023 17:41 UTC
7 points
0 comments4 min readLW link

The Goal Mis­gen­er­al­iza­tion Problem

Myspy18 May 2023 23:40 UTC
1 point
0 comments1 min readLW link

A Mechanis­tic In­ter­pretabil­ity Anal­y­sis of a GridWorld Agent-Si­mu­la­tor (Part 1 of N)

Joseph Bloom16 May 2023 22:59 UTC
36 points
2 comments16 min readLW link

We Shouldn’t Ex­pect AI to Ever be Fully Rational

OneManyNone18 May 2023 17:09 UTC
19 points
31 comments6 min readLW link

[Question] Is “brit­tle al­ign­ment” good enough?

the8thbit23 May 2023 17:35 UTC
9 points
5 comments3 min readLW link

Two ideas for al­ign­ment, per­pet­ual mu­tual dis­trust and induction

APaleBlueDot25 May 2023 0:56 UTC
1 point
2 comments4 min readLW link

[AN #67]: Creat­ing en­vi­ron­ments in which to study in­ner al­ign­ment failures

Rohin Shah7 Oct 2019 17:10 UTC
17 points
0 comments8 min readLW link

how hu­mans are aligned

bhauth26 May 2023 0:09 UTC
14 points
3 comments1 min readLW link

Shut­down-Seek­ing AI

Simon Goldstein31 May 2023 22:19 UTC
48 points
31 comments15 min readLW link

How will they feed us

meijer19731 Jun 2023 8:49 UTC
4 points
3 comments5 min readLW link

Ex­am­ples of AI’s be­hav­ing badly

Stuart_Armstrong16 Jul 2015 10:01 UTC
41 points
41 comments1 min readLW link

A Mul­tidis­ci­plinary Ap­proach to Align­ment (MATA) and Archety­pal Trans­fer Learn­ing (ATL)

MiguelDev19 Jun 2023 2:32 UTC
4 points
2 comments7 min readLW link

Lo­cal­iz­ing goal mis­gen­er­al­iza­tion in a maze-solv­ing policy network

jan betley6 Jul 2023 16:21 UTC
37 points
2 comments7 min readLW link

Safely and use­fully spec­tat­ing on AIs op­ti­miz­ing over toy worlds

AlexMennen31 Jul 2018 18:30 UTC
24 points
16 comments2 min readLW link

Sim­ple al­ign­ment plan that maybe works

Iknownothing18 Jul 2023 22:48 UTC
4 points
8 comments1 min readLW link

Visi­ble loss land­scape bas­ins don’t cor­re­spond to dis­tinct algorithms

Mikhail Samin28 Jul 2023 16:19 UTC
65 points
13 comments4 min readLW link

En­hanc­ing Cor­rigi­bil­ity in AI Sys­tems through Ro­bust Feed­back Loops

Justausername24 Aug 2023 3:53 UTC
1 point
0 comments6 min readLW link

Mesa-Op­ti­miza­tion: Ex­plain it like I’m 10 Edition

brook26 Aug 2023 23:04 UTC
20 points
1 comment6 min readLW link

“In­ner Align­ment Failures” Which Are Ac­tu­ally Outer Align­ment Failures

johnswentworth31 Oct 2020 20:18 UTC
66 points
38 comments5 min readLW link

High-level in­ter­pretabil­ity: de­tect­ing an AI’s objectives

28 Sep 2023 19:30 UTC
69 points
4 comments21 min readLW link

A Case for AI Safety via Law

JWJohnston11 Sep 2023 18:26 UTC
17 points
12 comments4 min readLW link

In­ter­nal Tar­get In­for­ma­tion for AI Oversight

Paul Colognese20 Oct 2023 14:53 UTC
15 points
0 comments5 min readLW link

(Non-de­cep­tive) Subop­ti­mal­ity Alignment

Sodium18 Oct 2023 2:07 UTC
3 points
1 comment8 min readLW link

Thoughts On (Solv­ing) Deep Deception

Jozdien21 Oct 2023 22:40 UTC
65 points
2 comments6 min readLW link

AI Align­ment Us­ing Re­v­erse Simulation

Sven Nilsen12 Jan 2021 20:48 UTC
0 points
0 comments1 min readLW link

For­mal Solu­tion to the In­ner Align­ment Problem

michaelcohen18 Feb 2021 14:51 UTC
49 points
123 comments2 min readLW link

Re­sponse to “What does the uni­ver­sal prior ac­tu­ally look like?”

michaelcohen20 May 2021 16:12 UTC
36 points
33 comments18 min readLW link

In­suffi­cient Values

16 Jun 2021 14:33 UTC
31 points
15 comments5 min readLW link

Call for re­search on eval­u­at­ing al­ign­ment (fund­ing + ad­vice available)

Beth Barnes31 Aug 2021 23:28 UTC
105 points
11 comments5 min readLW link

Ob­sta­cles to gra­di­ent hacking

leogao5 Sep 2021 22:42 UTC
28 points
11 comments4 min readLW link