Let us look at a sample code:

 >>>from gensim.models import word2vec

 #let us train a sample model like yours
 >>>sentences = [['first', 'sentence'], ['second', 'sentence']]
 >>>model1 = word2vec.Word2Vec(sentences, min_count=1)

 #let this be the model from which you want to reset
 >>>sentences = [['third', 'sentence'], ['fourth', 'sentence']]
 >>>model2 = word2vec.Word2Vec(sentences, min_count=1)
 >>>model1.reset_from(model2)
 >>>model1.similarity('third','sentence')
 -0.064622000988260417

Hence, we observe that `model1` is being reset by the `model2` and hence the word, `'third'` and `'sentence'` are in it's vocabulary eventually giving its similarity. This is the basic use, you can also check `reset_weights()` to reset the weights to untrained/initial state.