Well, first of all, this is something you do while running the model. Sure, it’s the same model, but it’s still two different processes to run in parallel.

Then, from what I gather, it’s closer to model finetuning than it is to inference. And if you look up the figures, finetune requires a lot more power and VRAM. As I said, it’s rewriting the neural network, which is the definition of finetuning.

So in order to get a more specific answer, we should look up why finetuning requires more than inference.

there’s lots of different kinds of RL algos with different requirements

In general though, the tradeoff you’re making is: data efficiency vs compute complexity

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On one end, evolutionary methods & gradient-free optimization methods are simple, but data hungry.

On the other end, are things like model based RL (eg building reward models to train your generator model) are more data efficient, but are more complex since they have more moving parts and more live models to train.

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So to answer:

Seriously though, what makes it require more VRAM than regular inference? You’re still loading the same model, aren’t you?

No, on the model-based end, you’re training at least 2 models: the generator and the reward model.

On the evolutionary & gradient free end, you need far more data than supervised learning, since reinforcement learning doesn’t tell the agent what to do at every time step, only after N time steps, so you’re getting basically 1/Nth the training signal for each step compared to supervised learning.

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Basically, we as GPU poors are in the wierd position where anything we can train under these limitations would probably have worse performance than just training a larger model off supervised datasets