We study the problem of learning efficient algorithms that strongly generalize in the framework of neural program induction. By carefully designing the input / output interfaces of the neural model and through imitation, we are able to learn models that produce correct results for arbitrary input sizes, achieving strong generalization. Moreover, by using reinforcement learning, we optimize for program efficiency metrics, and discover new algorithms that surpass the teacher used in imitation. With this, our approach can learn to outperform custom-written solutions for a variety of problems, as we tested it on sorting, searching in ordered lists and the NP-complete 0/1 knapsack problem, which sets a notable milestone in the field of Neural Program Induction. As highlights, our learned model can perform sorting perfectly on any input data size we tested on, with $O(n log n)$ complexity, whilst outperforming hand-coded algorithms, including quick sort, in number of operations even for list sizes far beyond those seen during training.
Measuring Sample Efficiency and Generalization in Reinforcement Learning Benchmarks: NeurIPS 2020 Procgen Benchmark
AIcrowd Research, AIcrowd（AIcrowd Research，AIcrowd）；Toyota Research Institute（丰田研究所）；Microsoft Research（微软研究院）；Three Thirds；Institute of Informatics, Universidade Federal do Rio Grande do Sul（南里奥格兰德联邦大学信息学研究所）；Temple University（天普大学）；AIcrowd Community（AIcrowd社区）；DiDi Labs（滴滴实验室）
Improving Generalization and Data Efficiency with Diffusion in Offline Multi-agent RL
Institute for Interdisciplinary Information Sciences（交叉信息学院）；Tsinghua University（清华大学）；Department of Electronic and Computer Engineering（电子与计算机工程系）；Hong Kong University of Science and Technology（香港科学与技术大学）
DeGuV: Depth-Guided Visual Reinforcement Learning for Generalization and Interpretability in Manipulation
Cognitive Robotics Lab, School of Computer Science, University of Manchester（认知机器人实验室，计算机科学学院，曼彻斯特大学）；Learning and Adaptive Robotics Lab, Department of Computer Science and Engineering, University of Texas at Arlington（学习与自适应机器人实验室，计算机科学与工程系，德克萨斯大学阿灵顿分校）
Theoretical Foundation of Flow-Based Time Series Generation: Provable Approximation, Generalization, and Efficiency
South China University of Technology（华南理工大学）；The Simons Institute for the Theory of Computing at the UC, Berkeley（加州大学伯克利分校西蒙斯理论计算研究所）；Sun Yat-sen University（中山大学）
本文针对基于流的时间序列生成缺乏理论解释的问题，建立涵盖逼近、泛化与效率的理论框架，证明 DiT 任意误差拟合、多项式正则化泛化界及采样快速收敛。
Enhancing Parameter Efficiency and Generalization in Large-Scale Models: A Regularized and Masked Low-Rank Adaptation Approach
Tsinghua University（清华大学）；Tsinghua-Berkeley Shenzhen Institute（清华-伯克利深圳学院）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；Institute for AI Industry Research (AIR)（人工智能产业研究院）；Shanghai AI Lab（上海人工智能实验室）
The Golden Subspace: Where Efficiency Meets Generalization in Continual Test-Time Adaptation
School of Artificial Intelligence, Nanjing University（南京大学人工智能学院）；National Key Laboratory for Novel Software Technology, Nanjing University（南京大学新型软件技术国家重点实验室）；Hong Kong University of Science and Technology（香港科技大学）；Frontier Robotics（前沿机器人）
