# A Review of the Applications of Deep Learning-Based Emergent Communication arXiv: https://arxiv.org/abs/2407.03302 ยท HTML: https://mlsanity.com/paper/2407.03302/ ## Key facts - Cited by 17 papers in the map (data through 14 Oct 2026). - Placed in RL training of language and agent models, one of 15 directions in Deep reinforcement learning methods. - Builds on 143 of its 216 references that are in the map. - First posted on arXiv 3 Jul 2024. ## Placed in - Deep reinforcement learning methods / [RL training of language and agent models](https://mlsanity.com/deeprl/rl-training-of-language-and-agent-models/) ## Builds on (most cited first, 30 of 143) - [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019) - [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022) - [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016) - [World Models](https://mlsanity.com/paper/1803.10122/) (2018) - [The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables](https://mlsanity.com/paper/1611.00712/) (2016) - [Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm](https://mlsanity.com/paper/1712.01815/) (2017) - [On the Cross-lingual Transferability of Monolingual Representations](https://mlsanity.com/paper/1910.11856/) (2019) - [Learning to Communicate with Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1605.06676/) (2016) - [How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection](https://mlsanity.com/paper/2301.07597/) (2023) - [Emergent Tool Use From Multi-Agent Autocurricula](https://mlsanity.com/paper/1909.07528/) (2019) - [MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learning](https://mlsanity.com/paper/2109.12674/) (2021) - [Emergence of Grounded Compositional Language in Multi-Agent Populations](https://mlsanity.com/paper/1703.04908/) (2017) - [Experience Grounds Language](https://mlsanity.com/paper/2004.10151/) (2020) - [Multi-Agent Cooperation and the Emergence of (Natural) Language](https://mlsanity.com/paper/1612.07182/) (2016) - [Emergent Multi-Agent Communication in the Deep Learning Era](https://mlsanity.com/paper/2006.02419/) (2020) - [Emergence of Language with Multi-agent Games: Learning to Communicate with Sequences of Symbols](https://mlsanity.com/paper/1705.11192/) (2017) - [Unveiling Transformers with LEGO: a synthetic reasoning task](https://mlsanity.com/paper/2206.04301/) (2022) - [Compositionality and Generalization in Emergent Languages](https://mlsanity.com/paper/2004.09124/) (2020) - [Emergence of Linguistic Communication from Referential Games with Symbolic and Pixel Input](https://mlsanity.com/paper/1804.03984/) (2018) - [What Artificial Neural Networks Can Tell Us About Human Language Acquisition](https://mlsanity.com/paper/2208.07998/) (2022) - [Natural Language Does Not Emerge 'Naturally' in Multi-Agent Dialog](https://mlsanity.com/paper/1706.08502/) (2017) - [Measuring Compositionality in Representation Learning](https://mlsanity.com/paper/1902.07181/) (2019) - [Compositional Languages Emerge in a Neural Iterated Learning Model](https://mlsanity.com/paper/2002.01365/) (2020) - [On the Pitfalls of Measuring Emergent Communication](https://mlsanity.com/paper/1903.05168/) (2019) - [Ease-of-Teaching and Language Structure from Emergent Communication](https://mlsanity.com/paper/1906.02403/) (2019) - [Emergent Communication through Negotiation](https://mlsanity.com/paper/1804.03980/) (2018) - [Anti-efficient encoding in emergent communication](https://mlsanity.com/paper/1905.12561/) (2019) - [Linguistic generalization and compositionality in modern artificial neural networks](https://mlsanity.com/paper/1904.00157/) (2019) - [Countering Language Drift with Seeded Iterated Learning](https://mlsanity.com/paper/2003.12694/) (2020) - [Countering Language Drift via Visual Grounding](https://mlsanity.com/paper/1909.04499/) (2019) ## Built on by (most cited first, 17 of 17) - [The Five Ws of Multi-Agent Communication: Who Talks to Whom, When, What, and Why -- A Survey from MARL to Emergent Language and LLMs](https://mlsanity.com/paper/2602.11583/) (2026) - [Simulating the Emergence of Differential Case Marking with Communicating Neural-Network Agents](https://mlsanity.com/paper/2502.04038/) (2025) - [Unsupervised Translation of Emergent Communication](https://mlsanity.com/paper/2502.07552/) (2025) - [ELCC: the Emergent Language Corpus Collection](https://mlsanity.com/paper/2407.04158/) (2024) - [Investigating the Development of Task-Oriented Communication in Vision-Language Models](https://mlsanity.com/paper/2601.20641/) (2026) - [SimSiam Naming Game: A Unified Approach for Emergent Communication and Representation Learning](https://mlsanity.com/paper/2410.21803/) (2024) - [Metropolis-Hastings Captioning Game: Knowledge Fusion of Vision Language Models via Decentralized Bayesian Inference](https://mlsanity.com/paper/2504.09620/) (2025) - [From Grunts to Lexicons: Emergent Language from Cooperative Foraging](https://mlsanity.com/paper/2505.12872/) (2025) - [Searching for the Most Human-like Emergent Language](https://mlsanity.com/paper/2510.03467/) (2025) - [SANEmerg: An Emergent Communication Framework for Semantic-aware Agentic AI Networking](https://mlsanity.com/paper/2605.05861/) (2026) - [Cognitively-Inspired Emergent Communication via Knowledge Graphs for Assisting the Visually Impaired](https://mlsanity.com/paper/2505.22087/) (2025) - [Why Are Parsing Actions for Understanding Message Hierarchies Not Random?](https://mlsanity.com/paper/2506.22366/) (2025) - [Discovering Properties of Inflectional Morphology in Neural Emergent Communication](https://mlsanity.com/paper/2508.05843/) (2025) - [Morpheme Induction for Emergent Language](https://mlsanity.com/paper/2510.03439/) (2025) - [The emergence of numerical representations in communicating artificial agents](https://mlsanity.com/paper/2602.10996/) (2026) - [Drawing with Strangers: Population Scaling Drives Zero-Shot Mutual Intelligibility in Emergent Sketching](https://mlsanity.com/paper/2606.10582/) (2026) - [A Dynamic-Semantics Framework for Grounding Human Referring Expressions in Visual Perceptual Data](https://mlsanity.com/paper/2608.08663/) (2026) ## Abstract (arXiv) Emergent communication, or emergent language, is the field of research which studies how human language-like communication systems emerge de novo in deep multi-agent reinforcement learning environments. The possibilities of replicating the emergence of a complex behavior like language have strong intuitive appeal, yet it is necessary to complement this with clear notions of how such research can be applicable to other fields of science, technology, and engineering. This paper comprehensively reviews the applications of emergent communication research across machine learning, natural language processing, linguistics, and cognitive science. Each application is illustrated with a description of its scope, an explication of emergent communication's unique role in addressing it, a summary of the extant literature working towards the application, and brief recommendations for near-term research directions.