Risks from Learned Optimization in Advanced ML Systems
Evan Hubinger, Chris van Merwijk, Vladimir Mikulik, Joar Skalse, and Scott Garrabrant
We analyze the type of learned optimization that occurs when a learned model (such as a neural network) is itself an optimizer—a situation we refer to as mesa-optimization. We believe that the possibility of mesa-optimization raises two important questions for the safety and transparency of advanced machine learning systems. First, under what circumstances will learned models be optimizers, including when they should not be? Second, when a learned model is an optimizer, what will its objective be—how will it differ from the loss function it was trained under—and how can it be aligned? In this paper, we provide an in-depth analysis of these two primary questions and provide an overview of topics for future research.
Section 1 Glossary:
- Base optimizer: A base optimizer is an optimizer that searches through
algorithms according to some objective.
- Base objective: A base objective is the objective of a base optimizer.
- Behavioral objective: The behavioral objective is what an optimizer appears to be optimizing for. Formally, the behavioral objective is the objective recovered from perfect inverse reinforcement learning.
- Inner alignment: The inner alignment problem is the problem of aligning the base and mesa- objectives of an advanced ML system.
- Learned algorithm: The algorithms that a base optimizer is searching through are called learned algorithms.
- Mesa-optimizer: A mesa-optimizer is a learned algorithm that is itself an optimizer.
- Mesa-objective: A mesa-objective is the objective of a mesa-optimizer.
- Meta-optimizer: A meta-optimizer is a system which is tasked with producing a base optimizer.
- Optimizer: An optimizer is a system that internally searches through some space of possible outputs, policies, plans, strategies, etc. looking for those that do well according to some internally-represented objective function.
- Outer alignment: The outer alignment problem is the problem of aligning the base objective of an advanced ML system with the desired goal of the programmers.
- Pseudo-alignment: A mesa-optimizer is pseudo-aligned with the base objective if it appears aligned on the training data but is not robustly aligned.
- Robust alignment: A mesa-optimizer is robustly aligned with the base objective if it robustly optimizes for the base objective across distributions.
Section 2 Glossary:
- Algorithmic range: The algorithmic range of a machine learning system refers to how extensive the set of algorithms capable of being found be the base optimizer is.
- Local optimization process: A local optimization process is an optimizer that uses local hill-climbing as its means of search.
- Reachability: The reachability of a learned algorithm refers to the difficulty for the base optimizer to find that learned algorithm.
Section 3 Glossary:
- Approximate alignment: An approximately aligned mesa-optimizer is a pseudo-aligned mesa-optimizer where the base and mesa- objectives are approximately the same up to some degree of approximation error due to the difficulty of representing the base objective in the mesa-optimizer.
- Proxy alignment: A proxy aligned mesa-optimizer is a pseudo-aligned mesa-optimizer that has learned to optimize for some proxy of the base objective instead of the base objective itself.
- Instrumental alignment: Instrumental alignment is a type of proxy alignment in which the mesa-optimizer optimizes the proxy as an instrumental goal of increasing the mesa-objective in the training distribution.
- Side-effect alignment: Side-effect alignment is a type of proxy alignment in which optimizing for the mesa-objective has the direct causal result of increasing the base objective in the training distribution.
- Suboptimality alignment: A suboptimality aligned mesa-optimizer is a pseudo-aligned mesa-optimizer in which some deficiency, error, or limitation causes it to exhibit aligned behavior.
Section 4 Glossary:
- Corrigible alignment: A corrigibly aligned mesa-optimizer is a robustly aligned mesa-optimizer that has a mesa-objective that “points to” its epistemic model of the base objective.
- Deceptive alignment: A deceptively aligned mesa-optimizer is a pseudoaligned mesa-optimizer that has enough information about the base objective to seem more fit from the perspective of the base optimizer than it actually is.
- Internal alignment: An internally aligned mesa-optimizer is a robustly aligned mesa-optimizer that has internalized the base objective in its mesa-objective.
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