Enhancing the planning capabilities of large language models by building external world models
Bibliographic record
Abstract
Large Language Models (LLMs) possess a huge amount of knowledge but struggle with multi-step planning even in toy environments due to the limitations of their static internal world model. We introduce a novel approach where an LLM serves as a “world model builder”, constructing and iteratively refining an explicit, external world model. The core of our approach is a state transition function, that is initially generated by the LLM and is refined using feedback from interactions with the environment. This refinement is made possible by accumulating test cases from past experiences allowing us to treat the construction of the world model as a program synthesis problem. We demonstrate the efficacy of our method on the Blocksworld benchmark and introduce a novel ColorMixing dataset that is designed to evaluate multi-step reasoning and planning. Our experimental results show that our method, using GPT-4 and LLaMA3- 70B, achieves perfect accuracy on Blocksworld tasks and significantly outperforms baseline methods, especially in terms of planning success and LLM queries. This paper presents a robust methodology for enhancing LLM planning via a learnable external world model and contributes a new benchmark for evaluating such capabilities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".