# A Survey on Compositional Generalization in Applications

arXiv: https://arxiv.org/abs/2302.01067 · HTML: https://mlsanity.com/paper/2302.01067/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 46 references that are in the map.
- First posted on arXiv 2 Feb 2023.

## Builds on (most cited first, 16 of 16)

- [Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations](https://mlsanity.com/paper/1811.12359/) (2018)
- [COGS: A Compositional Generalization Challenge Based on Semantic Interpretation](https://mlsanity.com/paper/2010.05465/) (2020)
- [Emergence of Language with Multi-agent Games: Learning to Communicate with Sequences of Symbols](https://mlsanity.com/paper/1705.11192/) (2017)
- [Evaluating the Impact of Model Scale for Compositional Generalization in Semantic Parsing](https://mlsanity.com/paper/2205.12253/) (2022)
- [Human few-shot learning of compositional instructions](https://mlsanity.com/paper/1901.04587/) (2019)
- [Compositional Generalization in Unsupervised Compositional Representation Learning: A Study on Disentanglement and Emergent Language](https://mlsanity.com/paper/2210.00482/) (2022)
- [On Compositional Generalization of Neural Machine Translation](https://mlsanity.com/paper/2105.14802/) (2021)
- [Compositional Generalization in Image Captioning](https://mlsanity.com/paper/1909.04402/) (2019)
- [Policy Architectures for Compositional Generalization in Control](https://mlsanity.com/paper/2203.05960/) (2022)
- [Toward Compositional Generalization in Object-Oriented World Modeling](https://mlsanity.com/paper/2204.13661/) (2022)
- [Compositional Multi-Object Reinforcement Learning with Linear Relation Networks](https://mlsanity.com/paper/2201.13388/) (2022)
- [SupervisorBot: NLP-Annotated Real-Time Recommendations of Psychotherapy Treatment Strategies with Deep Reinforcement Learning](https://mlsanity.com/paper/2208.13077/) (2022)
- [Knowledge Management System with NLP-Assisted Annotations: A Brief Survey and Outlook](https://mlsanity.com/paper/2206.07304/) (2022)
- [Grounded Graph Decoding Improves Compositional Generalization in Question Answering](https://mlsanity.com/paper/2111.03642/) (2021)
- [Lightweight Compositional Embeddings for Incremental Streaming Recommendation](https://mlsanity.com/paper/2202.02427/) (2022)
- [Improving Compositional Generalization in Math Word Problem Solving](https://mlsanity.com/paper/2209.01352/) (2022)

## Built on by (most cited first, 12 of 12)

- [Entity-Centric Reinforcement Learning for Object Manipulation from Pixels](https://mlsanity.com/paper/2404.01220/) (2024)
- [On Provable Length and Compositional Generalization](https://mlsanity.com/paper/2402.04875/) (2024)
- [Reasoning in Computer Vision: Taxonomy, Models, Tasks, and Methodologies](https://mlsanity.com/paper/2508.10523/) (2025)
- [Compositional Risk Minimization](https://mlsanity.com/paper/2410.06303/) (2024)
- [Entity-based Reinforcement Learning for Autonomous Cyber Defence](https://mlsanity.com/paper/2410.17647/) (2024)
- [EC-Diffuser: Multi-Object Manipulation via Entity-Centric Behavior Generation](https://mlsanity.com/paper/2412.18907/) (2024)
- [Hierarchical Entity-centric Reinforcement Learning with Factored Subgoal Diffusion](https://mlsanity.com/paper/2602.02722/) (2026)
- [Controllable Image Generation with Composed Parallel Token Prediction](https://mlsanity.com/paper/2405.06535/) (2024)
- [Disassociating performance from compositional feature learning](https://mlsanity.com/paper/2505.09716/) (2025)
- [Controllable Image Generation with Composed Parallel Token Prediction](https://mlsanity.com/paper/2604.05730/) (2026)
- [Grounding vs. Compositionality: On the Non-Complementarity of Reasoning in Neuro-Symbolic Systems](https://mlsanity.com/paper/2604.26521/) (2026)
- [Scale Up Strategically: Learning Compositional Generalization via Bias-Aware Evaluation and Data Collection for Robotic Manipulation](https://mlsanity.com/paper/2607.21582/) (2026)

## Abstract (arXiv)

The field of compositional generalization is currently experiencing a renaissance in AI, as novel problem settings and algorithms motivated by various practical applications are being introduced, building on top of the classical compositional generalization problem. This article aims to provide a comprehensive review of top recent developments in multiple real-life applications of the compositional generalization. Specifically, we introduce a taxonomy of common applications and summarize the state-of-the-art for each of those domains. Furthermore, we identify important current trends and provide new perspectives pertaining to the future of this burgeoning field.