# Learning Language Games through Interaction

arXiv: https://arxiv.org/abs/1606.02447 · HTML: https://mlsanity.com/paper/1606.02447/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2016.

## Built on by (most cited first, 30 of 43)

- [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017)
- [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](https://mlsanity.com/paper/2204.01691/) (2022)
- [Cognitive Architectures for Language Agents](https://mlsanity.com/paper/2309.02427/) (2023)
- [CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities](https://mlsanity.com/paper/2201.06796/) (2022)
- [Learning Language-Conditioned Robot Behavior from Offline Data and Crowd-Sourced Annotation](https://mlsanity.com/paper/2109.01115/) (2021)
- [Inferring Rewards from Language in Context](https://mlsanity.com/paper/2204.02515/) (2022)
- [Read and Reap the Rewards: Learning to Play Atari with the Help of Instruction Manuals](https://mlsanity.com/paper/2302.04449/) (2023)
- [Learning Rewards from Linguistic Feedback](https://mlsanity.com/paper/2009.14715/) (2020)
- [Communicating Natural Programs to Humans and Machines](https://mlsanity.com/paper/2106.07824/) (2021)
- [Learning to Repair: Repairing model output errors after deployment using a dynamic memory of feedback](https://mlsanity.com/paper/2112.09737/) (2021)
- [LLF-Bench: Benchmark for Interactive Learning from Language Feedback](https://mlsanity.com/paper/2312.06853/) (2023)
- [Few-shot Language Coordination by Modeling Theory of Mind](https://mlsanity.com/paper/2107.05697/) (2021)
- [LISA: Learning Interpretable Skill Abstractions from Language](https://mlsanity.com/paper/2203.00054/) (2022)
- [Interactive Learning from Activity Description](https://mlsanity.com/paper/2102.07024/) (2021)
- [iVISPAR -- An Interactive Visual-Spatial Reasoning Benchmark for VLMs](https://mlsanity.com/paper/2502.03214/) (2025)
- [A Survey on Human Preference Learning for Large Language Models](https://mlsanity.com/paper/2406.11191/) (2024)
- [Continual Learning for Instruction Following from Realtime Feedback](https://mlsanity.com/paper/2212.09710/) (2022)
- [Continual Learning for Grounded Instruction Generation by Observing Human Following Behavior](https://mlsanity.com/paper/2108.04812/) (2021)
- [Learning to Simulate Natural Language Feedback for Interactive Semantic Parsing](https://mlsanity.com/paper/2305.08195/) (2023)
- [Draw Me a Flower: Processing and Grounding Abstraction in Natural Language](https://mlsanity.com/paper/2106.14321/) (2021)
- [FixMyPose: Pose Correctional Captioning and Retrieval](https://mlsanity.com/paper/2104.01703/) (2021)
- [Pragmatics in Language Grounding: Phenomena, Tasks, and Modeling Approaches](https://mlsanity.com/paper/2211.08371/) (2022)
- [Continually Improving Extractive QA via Human Feedback](https://mlsanity.com/paper/2305.12473/) (2023)
- [Multi-agent KTO: Reinforcing Strategic Interactions of Large Language Model in Language Game](https://mlsanity.com/paper/2501.14225/) (2025)
- [Interscript: A dataset for interactive learning of scripts through error feedback](https://mlsanity.com/paper/2112.07867/) (2021)
- [ASHA: Assistive Teleoperation via Human-in-the-Loop Reinforcement Learning](https://mlsanity.com/paper/2202.02465/) (2022)
- [First Contact: Unsupervised Human-Machine Co-Adaptation via Mutual Information Maximization](https://mlsanity.com/paper/2205.12381/) (2022)
- [Improving Neural Model Performance through Natural Language Feedback on Their Explanations](https://mlsanity.com/paper/2104.08765/) (2021)
- [Designing an Automatic Agent for Repeated Language based Persuasion Games](https://mlsanity.com/paper/2105.04976/) (2021)
- [Accelerating Reinforcement Learning Agent with EEG-based Implicit Human Feedback](https://mlsanity.com/paper/2006.16498/) (2020)

## Abstract (arXiv)

We introduce a new language learning setting relevant to building adaptive natural language interfaces. It is inspired by Wittgenstein's language games: a human wishes to accomplish some task (e.g., achieving a certain configuration of blocks), but can only communicate with a computer, who performs the actual actions (e.g., removing all red blocks). The computer initially knows nothing about language and therefore must learn it from scratch through interaction, while the human adapts to the computer's capabilities. We created a game in a blocks world and collected interactions from 100 people playing it. First, we analyze the humans' strategies, showing that using compositionality and avoiding synonyms correlates positively with task performance. Second, we compare computer strategies, showing how to quickly learn a semantic parsing model from scratch, and that modeling pragmatics further accelerates learning for successful players.