A Picture is Worth a Thousand Words: Language Models Plan from Pixels (2023.emnlp-main)
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| Challenge: | Recent work uses pre-trained language models to reason about plans from text instructions in embodied visual environments. |
| Approach: | They propose to use pre-trained language models to reason about plan sequences from text instructions in embodied visual environments. |
| Outcome: | The proposed approach outperforms previous approaches on the ALFWorld and VirtualHome benchmarks. |
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| Challenge: | Developing intelligent agents requires the ability to produce plans on the fly based on visual observations. |
| Approach: | They propose a language-first procedure planning framework with a modularized design . they first align current and goal observations with corresponding steps and then use a pre-trained LM to predict intermediate steps. |
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Model-Based Imaginative Planning for Embodied Agents (2026.acl-long)
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Junru Song, Hengzhe Jin, Yucong Huang, Tingsong Jiang, Weien Zhou, Feifei Wang, Yang Yang, Ying Wen, Wen Yao
| Challenge: | a lightweight world model converts raw pixels into object-centric symbolic states amenable to language-based reasoning . IMPLEMENT is a framework for grounding language agents in visual embodied environments . |
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Few-shot Subgoal Planning with Language Models (2022.naacl-main)
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| Challenge: | Pre-trained language models have shown successful progress in many text understanding benchmarks. |
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Recent Advances in Pre-trained Language Models: Why Do They Work and How Do They Work (2022.aacl-tutorials)
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| Challenge: | Pre-trained language models are language models that are pre-taught on large-scaled corpora in a self-supervised fashion. |
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LLMs as Planning Formalizers: A Survey for Leveraging Large Language Models to Construct Automated Planning Models (2025.findings-acl)
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| Challenge: | Large Language Models excel in various natural language tasks but struggle with long-horizon planning problems requiring structured reasoning. |
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| Challenge: | This tutorial explores how planning has been learned and deployed in creative workflows . many human creative tasks involve extensive planning, and actions need to be taken . |
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On the Limit of Language Models as Planning Formalizers (2025.acl-long)
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| Challenge: | Large Language Models can create plans that are neither executable nor verifiable in grounded environments. |
| Approach: | They use Large Language Models to generate a formal representation of the planning domain in some language, such as Planning Domain Definition Language (PDDL). |
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Do Language Models Understand Measurements? (2022.findings-emnlp)
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| Challenge: | Existing studies on numerical reasoning over text (NRoT) tests PLMs to understand numbers in contexts where numbers are an integral part of the context. |
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Reasoning with Language Model is Planning with World Model (2023.emnlp-main)
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| Challenge: | Large language models (LLMs) have shown remarkable reasoning capabilities, particularly with Chain-of-Thought-style prompts. |
| Approach: | They propose a framework that repurposes the LLM as both a world model and a reasoning agent and incorporates a principled planning algorithm (based on Monte Carlo Tree Search) |
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PARADISE: Evaluating Implicit Planning Skills of Language Models with Procedural Warnings and Tips Dataset (2024.findings-acl)
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| Challenge: | Recent studies have focused on whether large language models are capable of planning or executing plans. |
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