# WordCraft: An Environment for Benchmarking Commonsense Agents

arXiv: https://arxiv.org/abs/2007.09185 · HTML: https://mlsanity.com/paper/2007.09185/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2020.

## Built on by (most cited first, 14 of 14)

- [A Survey of Zero-shot Generalisation in Deep Reinforcement Learning](https://mlsanity.com/paper/2111.09794/) (2021)
- [BetterBench: Assessing AI Benchmarks, Uncovering Issues, and Establishing Best Practices](https://mlsanity.com/paper/2411.12990/) (2024)
- [Benchmarks for Automated Commonsense Reasoning: A Survey](https://mlsanity.com/paper/2302.04752/) (2023)
- [Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks](https://mlsanity.com/paper/2505.00234/) (2025)
- [A Systematic Survey of Text Worlds as Embodied Natural Language Environments](https://mlsanity.com/paper/2107.04132/) (2021)
- [Collective Innovation in Groups of Large Language Models](https://mlsanity.com/paper/2407.05377/) (2024)
- [Social Network Structure Shapes Innovation: Experience-sharing in RL with SAPIENS](https://mlsanity.com/paper/2206.05060/) (2022)
- [MAGELLAN: Metacognitive predictions of learning progress guide autotelic LLM agents in large goal spaces](https://mlsanity.com/paper/2502.07709/) (2025)
- [One-Shot Learning from a Demonstration with Hierarchical Latent Language](https://mlsanity.com/paper/2203.04806/) (2022)
- [lilGym: Natural Language Visual Reasoning with Reinforcement Learning](https://mlsanity.com/paper/2211.01994/) (2022)
- [Risk Management for Mitigating Benchmark Failure Modes: BenchRisk](https://mlsanity.com/paper/2510.21460/) (2025)
- [Leveraging class abstraction for commonsense reinforcement learning via residual policy gradient methods](https://mlsanity.com/paper/2201.12126/) (2022)
- [JECC: Commonsense Reasoning Tasks Derived from Interactive Fictions](https://mlsanity.com/paper/2210.15456/) (2022)
- [Beyond Temporal Proximity: Explanatory World Models for Credit Assignment in Reinforcement Learning](https://mlsanity.com/paper/2610.13504/) (2026)

## Abstract (arXiv)

The ability to quickly solve a wide range of real-world tasks requires a commonsense understanding of the world. Yet, how to best extract such knowledge from natural language corpora and integrate it with reinforcement learning (RL) agents remains an open challenge. This is partly due to the lack of lightweight simulation environments that sufficiently reflect the semantics of the real world and provide knowledge sources grounded with respect to observations in an RL environment. To better enable research on agents making use of commonsense knowledge, we propose WordCraft, an RL environment based on Little Alchemy 2. This lightweight environment is fast to run and built upon entities and relations inspired by real-world semantics. We evaluate several representation learning methods on this new benchmark and propose a new method for integrating knowledge graphs with an RL agent.