Recently, deep reinforcement learning (RL) has proven its feasibility in
solving combinatorial optimization problems (COPs). The learning-to-rank
techniques have been studied in the field of information retrieval. While
several COPs can be formulated as the prioritization of input items, as is
common in the information retrieval, it has not been fully explored how the
learning-to-rank techniques can be incorporated into deep RL for COPs. In this
paper, we present the learning-to-rank distillation-based COP framework, where
a high-performance ranking policy obtained by RL for a COP can be distilled
into a non-iterative, simple model, thereby achieving a low-latency COP solver.
Specifically, we employ the approximated ranking distillation to render a
score-based ranking model learnable via gradient descent. Furthermore, we use
the efficient sequence sampling to improve the inference performance with a
limited delay. With the framework, we demonstrate that a distilled model not
only achieves comparable performance to its respective, high-performance RL,
but also provides several times faster inferences. We evaluate the framework
with several COPs such as priority-based task scheduling and multidimensional
knapsack, demonstrating the benefits of the framework in terms of inference
latency and performance.

本文介绍了基于学习排序蒸馏的组合优化问题解决方法，其中高性能排序策略通过强化学习可以被提炼成非迭代简单模型，从而实现低延迟的拟合，通过优化推理效率和性能表现，证明了这一框架的优势。

一种利用排序学习蒸馏的高效组合优化模型

An Efficient Combinatorial Optimization Model Using Learning-to-Rank Distillation

Despite their effectiveness in a wide range of tasks, deep architectures
suffer from some important limitations. In particular, they are vulnerable to
catastrophic forgetting, i.e. they perform poorly when they are required to
update their model as new classes are available but the original training set
is not retained. This paper addresses this problem in the context of semantic
segmentation. Current strategies fail on this task because they do not consider
a peculiar aspect of semantic segmentation: since each training step provides
annotation only for a subset of all possible classes, pixels of the background
class (i.e. pixels that do not belong to any other classes) exhibit a semantic
distribution shift. In this work we revisit classical incremental learning
methods, proposing a new distillation-based framework which explicitly accounts
for this shift. Furthermore, we introduce a novel strategy to initialize
classifier's parameters, thus preventing biased predictions toward the
background class. We demonstrate the effectiveness of our approach with an
extensive evaluation on the Pascal-VOC 2012 and ADE20K datasets, significantly
outperforming state of the art incremental learning methods.

本文探讨了深度架构在更新模型上的遗忘问题和在语义分割中的特殊性，提出了一种基于蒸馏的框架解决语义分割中的背景类语义分布不一致的问题，并引入了一种新的分类器参数初始化策略，最终在 Pascal-VOC 2012 和 ADE20K 数据集上显著优于现有的增量学习方法。