this post was submitted on 09 Jun 2025
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I think the biggest problem is it's very low ability to "test time adaptability". Even when combined with a reasonning model outputting into its context, the weights do not learn out of the immediate context.
I think the solution might be to train a LoRa overlay on the fly against the weights and run inference with that AND the unmodified weights and then have an overseer model self evaluate and recompose the raw outputs.
Like humans are way better at answering stuff when it's a collaboration of more than one person. I suspect the same is true of LLMs.
It is.
It's really common for non-language implementations of neural networks. If you have an NN that's right some percentage of the time, you can often run it through a bunch of copies of the NNs and take the average and that average is correct a higher percentage of the time.
Aider is an open source AI coding assistant that lets you use one model to plan the coding and a second one to do the actual coding. It works better than doing it in a single pass, even if you assign the the same model to planing and coding.