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1. Lack of the support for multiple inputs, arbitrary outputs and nested iteration for both iteration APIs, which is required by scenarios like Metapath (multiple-inputs), boost algorithms (nested iteration) or when we want to output both loss and model (multiple-outputs). In the new iteration we would support these functionalities.
2. Lack of asynchronous iteration support for the DataSet iteration, which is required by algorithms like asynchronous linear regression, in the new iterations we would support both synchronous and asynchronous modes for the bounded iteration. 
3. The current DataSet iteration by default provides a "for each round" semantics, namely users only need to specify the computation logic in each iterationround, and the framework would executes the subgraph multiple times until convergence. To cooperate with the semantics, the DataSet iteration framework would merge the initial input and the feedback (bulk style and delta style), and replay the datasets comes from outside of the iteration. This method is easier to use, but it also limit some possible optimizations.

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