[Proposal] Method of locating useful subnets in large models

by Quintin Pope2 min read13th Oct 2021No comments

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Outer AlignmentTransparency / Interpretability (ML & AI)Reinforcement LearningMesa-OptimizationMachine LearningAI
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I’ve seen it suggested (e.g, here) that we could tackle the outer alignment problem by using interpretability tools to locate the learned “human values” subnet of powerful, unaligned models. Here I outline a general method of extracting such subnets from a large model.

Suppose we have a large, unaligned model M. We want to extract a small subnet from M that is useful for a certain task (T), which could be modeling human values, translating languages, etc. My proposal for finding such a subnet is to train a “subnet extraction” (SE) model through reinforcement learning.

We’d provide SE with access to M’s weights as well as an evaluation dataset for T. Presumably, SE would be a perceiver or similar architecture able to handle very large inputs and would only process small parts of M at a time.

During training, SE would select a small subnet from M, then the subnet would be sandwiched inside an “assister model” (AM), which consists of a pretrained encoder, followed by randomly initialized layers, followed by the extracted subnet, followed by more randomly initialized layers. The AM is then finetuned on the dataset for T as well as on a set of distractor datasets, {D_1, … D_n}. SE would get reward for AM’s post-finetuning performance on T’s dataset minus its average performance on the distractor datasets and be penalized according to the size of the subnet.

R = finetune(AM, T’s dataset) - a avg_val{finetune(AM, D_i), for i 1 to n} - b |subnet|

Where a and b are hyperparameters.

The idea is that the subnet M uses to solve T can be easily adapted to solve T in other contexts. It’s possible such subnets rely on features generated by other parts of M. That’s why I sandwich the subnet in AM. It’s supposed to provide the subnet with generic features, so that SE doesn’t have to extract those generic features from M.

I include the distractor datasets to ensure SE learns to extract subnets that are specific to the task/dataset provided and not just extract subnets from M that are really good for learning any task. I encourage SE to extract smaller subnets because I expect smaller subnets will be easier to analyze with other interpretability tools and because I think smaller subnets are less likely to include risky spillover from M (e.g., mesa optimizers). During training, we'd cycle the evaluation task/dataset with the aim that SE learn to be a general subnet extractor for whatever dataset it's given.

When we want to extract the human values subnet, we'd give SE a dataset that we think requires human value modeling to solve. We'd then continue SE's training process, providing SE with reward for the subnets it extracts. Potentially, we could increase b over time to prompt SE to extract the minimum size subnet that represents human values.

One potential risk is that there’s a mesa optimizer in M that trains SE to extract it by being very good at T while deliberately failing on the distractors. To address this issue, we can compare the subnets extracted for various tasks/datasets to see if they share weights and add a term to SE’s reward that encourages diversity in the subnets it extracts for different tasks.

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