text: The authors propose a new episodic reinforcement learning algorithm based on contextual bandit oracles. The key specificity of this algorithm is its ability to deal with the credit assignment problem by learning automatically a progressive "reward shaping" (the residual losses) from a feedback that is only provided at the end of the epochs. The paper is dense but well written. The theoretical grounding is a bit thin or hard to follow. The authors provide a few regret theoretical results (that I did not check deeply) obtained by reduction to "value-aware" contextual bandits. The experimental section is solid. The method is evaluated on several RL environments against state of the art RL algorithms. It is also evaluated on bandit structured prediction tasks. An interesting synthetic experiment (Figure 4) is also proposed to study the ability of the algorithm to work on both decomposable and non-decomposable structured prediction tasks. Question 1: The credit assignment approach you propose seems way more sophisticated than eligibility traces in TD learning. But sometimes old and simple methods are not that bad. Could you develop a bit on the relation between RESLOPE and eligibility traces ? Question 2: RESLOPE is built upon contextual bandits which require a stationary environment. Does RESLOPE inherit from this assumption? Typos: page 1 "scalar loss that output." -> "scalar loss." ", effectively a representation" -> ". By effective we mean effective in term of credit assignment." page 5 "and MTR" -> "and DR" page 6 "in simultaneously." -> ??? ".In greedy" -> ". In greedy"