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Mild Optimization

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Mild Optimization
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1plex

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
Most Relevant
8
100Soft optimization makes the value target biggerΩ
Jeremy Gillen
3mo
Ω
18
7
65When to use quantilizationΩ
RyanCarey
4y
Ω
5
2
67Steam
abramdemski
9mo
9
2
29Quantilizers maximize expected utility subject to a conservative cost constraintΩ
jessicata
8y
Ω
0
2
19Stable Pointers to Value III: Recursive QuantilizationΩ
abramdemski
5y
Ω
4
2
14Quantilal control for finite MDPsΩ
Vanessa Kosoy
5y
Ω
0
2
11Optimization Regularization through Time PenaltyΩ
Linda Linsefors
4y
Ω
4
2
2Thoughts on QuantilizersΩ
Stuart_Armstrong
6y
Ω
0
1
95The Optimizer's Curse and How to Beat It
lukeprog
12y
82
1
33Satisficers want to become maximisers
Stuart_Armstrong
11y
68
1
21Exploring Mild Behaviour in Embedded AgentsΩ
Megan Kinniment
9mo
Ω
4
1
17Reward is not Necessary: How to Create a Compositional Self-Preserving Agent for Life-Long LearningΩ
Roman Leventov
3mo
Ω
2
1
14Validator models: A simple approach to detecting goodharting
beren
1mo
1
1
4Breaking the Optimizer’s Curse, and Consequences for Existential Risks and Value Learning
Roger Dearnaley
1mo
0
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