non-positive-definite when arrows=2

Whenever I try to fit models and add covariances with arrows=2, I always have a heck of a time getting the model past the 'expected covariance is non-positive definite' error. Is this normal? I do not have nearly this much trouble with arrows = 1. Here is a little example using a built in dataset:

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summary(mxRun(m <- mxModel("Example", type = "RAM",
manifestVars = colnames(ability.cov$cov), latentVars = "G",
mxData(ability.cov$cov, type = "cov", numObs = ability.cov$n.obs),
mxPath(from = "G", to = colnames(ability.cov$cov)),
mxPath(from = colnames(ability.cov$cov), arrows = 2),
mxPath(from = "G", arrows = 2, values = 1, free = FALSE))))

summary(mxRun(mxModel(m, mxPath(from = "vocab", to = "reading", arrows = 2, values = 41))))
summary(mxRun(mxModel(m, mxPath(from = "vocab", to = "reading", arrows = 2, values = -.1))))
summary(mxRun(mxModel(m, mxPath(from = "vocab", to = "reading", arrows = 1, values = .4))))
summary(mxRun(mxModel(m, mxPath(from = "vocab", to = "reading", arrows = 1, values = 2))))

#

even knowing (from the second attempt), that one estimate that works well is 41.579037 and setting a starting value of 41, it runs into problems. Is there a reason it seems more sensitive to start values with covariances than just paths?