I try to do a model comparison with a model which has no discrimination parameters and one without (standard disc are set to 1 in brms - most users won’t even notice this, I think)

Monte Carlo SE of elpd_loo is NA.
Pareto k diagnostic values:
Count Pct. Min. n_eff
(-Inf, 0.5] (good) 3819 99.5% 1784
(0.5, 0.7] (ok) 17 0.4% 244
(0.7, 1] (bad) 4 0.1% 62
(1, Inf) (very bad) 0 0.0% <NA>
See help('pareto-k-diagnostic') for details.

So I used the loo_moment_match = true

Error in validate_ll(log_ratios) : All input values must be finite.
Fehler: Moment matching failed. Perhaps you did not set 'save_all_pars' to TRUE when fitting your model?

It seems to me there are some values that happen to be NA because or numerical instability. I am not sure what exactly is causing the problem though. You can try to install brms and loo from github and see if that fixes things already.

I estimated the whole model on a computer with windows 10 now and used the github versions of loo and brms. The first model was estimated on a macBook under Big Sur.
I do get the same error on the windows computer by using the moment_match function:

Error in checkForRemoteErrors(val) :
4 nodes produced errors; first error: All input values must be finite.
Fehler: Moment matching failed. Perhaps you did not set 'save_pars = save_pars(all = TRUE)' when fitting your model?

If I use the kfold-function like this:

kfold_uvsdt <- kfold(uvsdt, cores = 4)

Then the following message appears:

[1] "Error in sampler$call_sampler(args_list[[i]]) : Initialization failed."
[1] "error occurred during calling the sampler; sampling not done"
Start sampling
Fehler: The model does not contain posterior samples.

Some additional information:

I did a similar model without the disc parameters and was able to use the moment_match function

In the data we do have some participants with relatively high values on the latent variable (if this is an issue)

I am not an expert but I thought, that the model predicts our observered data very well: