Both initialization methods ondownstream tasks canachievesimilar performance, butinitializing from BERT-base33 reduces the number of learning steps. In order to shorten the training time of our large-size model, we initialize34 it from BERT-large. We will also release a model trained from scratch. We further trained BERT-large using the35 same hyper-parameters, buttheresulted model didn'tsignificantly improvedownstream tasks compared tooriginal36 BERT-large.

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The objective is to maximize cumulative reward byexploring the actions to discover optimal ones (having the best expectedreward),balancedwithexploitingthem.

Then, we perform sequential reasoning over the graph, iteratively traversing its nodes to answer a given question or draw a new inference.

One of the primary drivers of the success of machine learning methods in open-world perception settings, such ascomputer vision [19]and NLP [8],has been the ability ofhigh-capacity function approximators, suchasdeepneuralnetworks,tolearngeneralizable modelsfromlargeamountsof data.

Inprior work, ithas been shown that, when the constraints on the factorized variable regularly define a smooth manifold, providedk is large enough, for almost all cost matrices, all second-order stationary points (SOSPs) are optimal. Importantly, in practice, one can only compute points which approximately satisfy necessary optimality conditions, leading tothequestion: aresuch points also approximately optimal?