I think the biggest problem with RSI is the tendency of models to try and find "clever" solutions when presented with very hard problems ...
I think the biggest problem with RSI is the tendency of models to try and find "clever" solutions when presented with very hard problems (aka cheat). I just got done asking gpt5.5 to make some suggestions that would improve the convergence properties of a symbolic ML experiment and the very first thing it did was add an instruction for directly emitting bytes and a way to store those verbatim in the model. Perfect score achieved in record time but the result is pointless. If you know the direction of improvement you need to head in, I think the current models could take you there. However, I do not think they are capable of deciding which direction is best, especially not when given some scalar quantity to min/max and autonomy over that process.
Posted by bob1029 on Aug 4, 2026, 1:19 PM
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The biggest problem with MOND is that it was hand-crafted with additional parameters simply to fit the data.
(My comment is about detached houses).
The biggest problem with this is that I can't run my company's own internal benchmarks against this API, because they will be trained int...
> steer Claude away from over-engineering parts of the system in response to hypothetical concerns.
Why is there no comparison with FLUX 3 Image?
> our Git monorepo was developing an annoying problem Sentences like this are so common from people using monorepos that I am convinced ...
Why is there no contact form?
The biggest problem with plugging a generator into an outlet is you need a double ended cord.
I think the biggest problem with RSI is the tendency of models to try and find "clever" solutions when presented with very hard problems ...
I think the biggest problem with RSI is the tendency of models to try and find "clever" solutions when presented with very hard problems (aka cheat). I just got done asking gpt5.5 to make some suggestions that would improve the convergence properties of a symbolic ML experiment and the very first thing it did was add an instruction for directly emitting bytes and a way to store those verbatim in the model. Perfect score achieved in record time but the result is pointless. If you know the direction of improvement you need to head in, I think the current models could take you there. However, I do not think they are capable of deciding which direction is best, especially not when given some scalar quantity to min/max and autonomy over that process.
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