Produced as part of the SERI ML Alignment Theory Scholars Program - Winter 2022 Cohort.

Thanks to Jérémy Scheurer, Nicholas Dupuis and Evan Hubinger for feedback and discussion

When people talk about mesa-optimization, they sometimes say things like “we’re searching for the optimizer module” or “we’re doing interpretability to find out whether the network can do internal search”. An uncharitable interpretation of these claims is that the researchers expect the network to have something like an “optimization module” or “internal search algorithm” that is clearly different and distinguishable from the rest of the network (to be clear, we think it is fine to start with probably wrong mechanistic models).

In this post, we want to argue why we should not expect mesa-optimization to be modular or clearly different from the rest of the network (at least in transformers and CNNs) and that current architectures can already do mesa-optimization in a meaningful way. We think this implies that

Mesa-optimization improves gradually where more powerful models likely develop more powerful mesa optimizers.
Mesa-optimization should not be treated as a phenomenon of the future. Current models likely already do it, just in a very messy and distributed fashion.
When we look for mesa optimization, we probably have to look for a messy stack of heuristics combined with search-like abilities rather than clean Monte Carlo Tree Search (MCTS)-like structures.

We think most of our core points can be conveyed in a simple analogy. Imagine a human chess grandmaster that has to choose their moves in 1 second. In this second, they are probably not running a sophisticated tree search in their head, they rely on heuristics. These heuristics were shaped by years of playing the game and are often the result of doing explicit tree searches with more time. The resulting decision-making process is a heuristic that approximates or was at least shaped by optimization but is not an optimizer itself. This is approximately what we think mesa-optimization might look like in current neural networks, i.e. the model uses heuristics that have aspects of or approximate parts of optimization, but are not “clean”^[1] in the way e.g. MCTS is.

What is an accurate definition of mesa-optimization?

In risks from learned optimization mesa-optimization is characterized as

[...] it...

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