mlsanityData to 7 Oct 2026

ALFWorld: Aligning Text and Embodied Environments for Interactive Learning

arXiv 2010.03768 ↗First posted 8 Oct 2020cs.CL cs.AI cs.CVcited by 1,143

On the map: World models → World models for language agents

Abstract (arXiv)

Given a simple request like Put a washed apple in the kitchen fridge, humans can reason in purely abstract terms by imagining action sequences and scoring their likelihood of success, prototypicality, and efficiency, all without moving a muscle. Once we see the kitchen in question, we can update our abstract plans to fit the scene. Embodied agents require the same abilities, but existing work does not yet provide the infrastructure necessary for both reasoning abstractly and executing concretely. We address this limitation by introducing ALFWorld, a simulator that enables agents to learn abstract, text based policies in TextWorld (Cot'e et al., 2018) and then execute goals from the ALFRED benchmark (Shridhar et al., 2020) in a rich visual environment. ALFWorld enables the creation of a new BUTLER agent whose abstract knowledge, learned in TextWorld, corresponds directly to concrete, visually grounded actions. In turn, as we demonstrate empirically, this fosters better agent generalization than training only in the visually grounded environment. BUTLER's simple, modular design factors the problem to allow researchers to focus on models for improving every piece of the pipeline (language understanding, planning, navigation, and visual scene understanding).

Key facts. Cited by 1,143 papers in the map (data through 7 Oct 2026). Placed in World models for language agents, one of 11 directions in World models. Builds on 2 of its 55 references that are in the map. First posted on arXiv 8 Oct 2020.

Builds on

2 of 55 references.

References · 2

  1. 2017AI2-THOR: An Interactive 3D Environment for Visual AIcited by 850
  2. 2018Recurrent World Models Facilitate Policy Evolutioncited by 761

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Citing papers per year: 2021: 16; 2022: 27; 2023: 76; 2024: 168; 2025: 249; 2026: 607.

1,143 papers.

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