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Mild Optimization

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Mild Optimization
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2TurnTrout
1plex
You are viewing revision 1.2.0, last edited by plex

Mild optimization is an approach for mitigating Goodhart's law in AI alignment. Instead of maximizing a fixed objective, the hope is that the agent pursues the goal in a "milder" fashion.

Further reading: Arbital page on Mild Optimization

Posts tagged Mild Optimization
8
114Soft optimization makes the value target bigger
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Jeremy Gillen
9mo
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20
7
65When to use quantilization
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RyanCarey
5y
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5
3
37Satisficers want to become maximisers
Stuart_Armstrong
12y
70
2
73Steam
abramdemski
1y
9
2
33Quantilizers maximize expected utility subject to a conservative cost constraint
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jessicata
8y
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3
2
25Why don't quantilizers also cut off the upper end of the distribution?
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Alex_Altair, Jeremy Gillen
5mo
QΩ
2
2
20Stable Pointers to Value III: Recursive Quantilization
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abramdemski
5y
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4
2
14Quantilal control for finite MDPs
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Vanessa Kosoy
5y
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0
2
11Optimization Regularization through Time Penalty
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Linda Linsefors
5y
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4
2
2Thoughts on Quantilizers
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Stuart_Armstrong
6y
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0
1
96The Optimizer's Curse and How to Beat It
lukeprog
12y
84
1
58Thinking about maximization and corrigibility
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James Payor
5mo
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4
1
35AISC team report: Soft-optimization, Bayes and Goodhart
Simon Fischer, benjaminko, jazcarretao, DFNaiff, Jeremy Gillen
3mo
0
1
21Exploring Mild Behaviour in Embedded Agents
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Megan Kinniment
1y
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4
1
17Reward is not Necessary: How to Create a Compositional Self-Preserving Agent for Life-Long Learning
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Roman Leventov
9mo
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2
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