Grounding Large Language Models In Embodied Environment With Imperfect World Models

arXiv 24.10 2024 49.9 method, application

TLDR

GLIMO uses proxy world models (simulators) to generate training data via an LLM agent with self-refinement, improving LLM performance on embodied tasks.

Reasoning

The paper presents a novel approach to grounding LLMs using imperfect world models for data generation, with strong empirical results. However, reliance on simulators may limit real-world generalization, and the method's applicability beyond embodied tasks is unclear.

Read-first score

Read-first score 49.9, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 28.

Recency 8%

75.1

Uses a gentle age decay so recent papers surface without erasing older foundations. 2024

Methodology quality 25%

Screens visible abstract and analysis fields for experiment, dataset, baseline, metric, and limitation evidence. markers=benchmark,dataset,experiment

Topical relevance 42%

Uses existing LLM keyword relevance scores normalized to 0-100. world model,world simulator,generative world model,interactive world model,video world model,world dynamics prediction,model-based reinforcement learning world model

Reproducibility 25%

Screens links and visible text for paper, code, dataset, artifact, and repository signals. pdf=True; code=False; dataset=False; markers=dataset

Field roles

Methodology anchor

Rank sensitivity

Stability: volatile; rank range: 294.

Keyword Scores

world simulator

world model

interactive world model

generative world model

world dynamics prediction

video world model

model-based reinforcement learning world model

Deep Analysis

Innovations

Proposes GLIMO that uses proxy world models (simulators) to ground LLMs in embodied environments despite model imperfections
LLM agent-based data generator with iterative self-refining module for temporally consistent experience sampling
Diverse set of question-answering instruction seeds and retrieval-augmented generation module for reflecting on prior experiences

Methodology

GLIMO leverages imperfect world models (e.g., simulators) to collect and synthesize training data. It includes an LLM agent-based data generator that automatically creates high-quality and diverse instruction datasets, featuring an iterative self-refining module for temporally consistent experience sampling, diverse QA instruction seeds, and a retrieval-augmented generation module for reflecting on prior experiences.

Key Results

GLIMO improves strong open-source LLMs like LLaMA-3 by factors of 2.04×, 1.54×, and 1.82× across three different benchmarks, and its performance competes with or surpasses larger models such as GPT-4.

Resources

Paper