many HIGH RCs were needed in training to learn a HIGH bias, we first constrained all the RC attachment in the training data to HIGH attachment. Then, we varied the proportion (in increments of 10 RC sentences at a time) of RC sentences to filler sentences during training. We trained 5 RNNs for each training configuration (i.e. each proportion of RCs). This experiment provided a lower bound on the number of HIGH RCs needed in the training data to overcome any RNN recency bias when all RCs exhibited HIGH attachment. When as little as 0.017% (20 sentences) of the data contained RCs with HIGH attachment, the test difference in surprisal between HIGH and LOW attachment significantly differed from zero ($p < 10^{-5}$, BayesFactor (BF) $> 100$),[^10] with a mean difference less than zero ($\mu = -2.24$). These results indicate that the models were able to acquire a HIGH bias with only 20/120000 examples of HIGH RC attachment.

In practice, we would like LMs to learn a preference even when the training data contains a mixture of HIGH and LOW attachment. To determine the proportion of RCs that must be HIGH to learn a HIGH bias, we fixed 10% of the 