In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

Ex Input:
We consider reinforcement learning and bandit structured prediction problems with very sparse loss feedback: only at the end of an episode. We introduce a novel algorithm, RESIDUAL LOSS PREDICTION (RESLOPE), that solves such problems by automatically learning an internal representation of a denser reward function. RESLOPE operates as a reduction to contextual bandits, using its learned loss representation to solve the credit assignment problem, and a contextual bandit oracle to trade-off exploration and exploitation. RESLOPE enjoys a no-regret reduction-style theoretical guarantee and outperforms state of the art reinforcement learning algorithms in both MDP environments and bandit structured prediction settings.

Ex Output:
We present a novel algorithm for solving reinforcement learning and bandit structured prediction problems with very sparse loss feedback.


Ex Input:
A generally intelligent learner should generalize to more complex tasks than it has previously encountered, but the two common paradigms in machine learning -- either training a separate learner per task or training a single learner for all tasks -- both have difficulty with such generalization because they do not leverage  the compositional structure of the task distribution. This paper introduces the compositional problem graph as a broadly applicable formalism to relate tasks of different complexity in terms of problems with shared subproblems. We propose the compositional generalization problem for measuring how readily old knowledge can be reused and hence built upon. As a first step for tackling compositional generalization, we introduce the compositional recursive learner, a domain-general framework for learning algorithmic procedures for composing representation transformations, producing a learner that reasons about what computation to execute by making analogies to previously seen problems. We show on a symbolic and a high-dimensional domain that our compositional approach can generalize to more complex problems than the learner has previously encountered, whereas baselines that are not explicitly compositional do not.

Ex Output:
We explore the problem of compositional generalization and propose a means for endowing neural network architectures with the ability to compose themselves to solve these problems.


Ex Input:
We show that generating English Wikipedia articles can be approached as a multi-
 document summarization of source documents. We use extractive summarization
 to coarsely identify salient information and a neural abstractive model to generate
 the article. For the abstractive model, we introduce a decoder-only architecture
 that can scalably attend to very long sequences, much longer than typical encoder-
 decoder architectures used in sequence transduction. We show that this model can
 generate fluent, coherent multi-sentence paragraphs and even whole Wikipedia
 articles. When given reference documents, we show it can extract relevant factual
 information as reflected in perplexity, ROUGE scores and human evaluations.

Ex Output:

We generate Wikipedia articles abstractively conditioned on source document text.