Summary Reasoning with Language Model Planning with World Model arxiv.org
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The Reasoning via Planning (RAP) framework combines large language models with planning to improve their abilities in action planning, math reasoning, and logical inference by addressing their lack of an internal world model.
Slides
Slide Presentation (8 slides)
Key Points
- Large language models (LLMs) struggle with tasks that require action planning, math reasoning, and logical inference.
- The RAP algorithm achieves a 64% success rate for 2/4/6-step plan generation in Blocksworld, outperforming CoT and GPT-4 with CoT by a 33%.
- The RAP framework combines LLMs with planning algorithms to strategically plan a coherent reasoning path for solving various reasoning tasks.
- Multiple sample answers from the world model can be used to determine the confidence of a reasoning step in LLMs.
- The roll-out policy used in experiments involves generating candidate actions and selecting the one with the highest local reward.
- RAP involves updating the current state by adding new block conditions and removing conditions that are no longer true.
- The framework bridges the gap between language models and planning by using a world model and Monte Carlo Tree Search to simulate states of the world and anticipate action outcomes.
- Various research papers discuss topics such as self-play reinforcement learning algorithms, generating robot task plans using language models, cognitive maps in rats and men, learning world models and planning with neural systems.
Summaries
37 word summary
Large language models (LLMs) lack an internal world model, which hinders their abilities in action planning, math reasoning, and logical inference. The Reasoning via Planning (RAP) framework aims to address this limitation by combining LLMs with planning
38 word summary
Large language models (LLMs) have impressive reasoning capabilities but struggle with action planning, math reasoning, and logical inference due to the lack of an internal world model. The proposed framework, Reasoning via Planning (RAP), combines LLMs with planning
451 word summary
Large language models (LLMs) have shown remarkable reasoning capabilities, but they struggle with tasks that require action planning, math reasoning, and logical inference. This is because LLMs lack an internal world model to predict the world state and simulate long-term
In Blocksworld, the RAP algorithm achieves a 64% success rate for 2/4/6-step plan generation, while CoT fails. RAP also outperforms GPT-4 with CoT by a 33%
This paper proposes a framework called Reasoning via Planning (RAP) that uses Language Model Models (LLMs) as world models to strategically plan a coherent reasoning path for solving various reasoning tasks. The RAP framework combines the LLMs with planning algorithms
The document discusses reasoning with language models (LLMs) using planning with a world model. The authors propose using multiple sample answers from the world model to determine the confidence of a reasoning step. They also suggest self-evaluation by the LLM, where
In the document "Reasoning with Language Model Planning with World Model," the authors discuss the roll-out policy used in their experiments, which involves generating candidate actions and selecting the one with the highest local reward. They also explain the back-propagation process,
In this document, the authors describe a language model planning approach called Reasoning with Language Model Planning with World Model (RAP). RAP involves updating the current state by adding new block conditions and removing conditions that are no longer true. The quality of actions
The excerpt discusses the limitations of a language model planning algorithm and introduces a new framework called RAP that addresses these limitations. RAP explores different paths and generates successful plans by calculating rewards based on the current state. The framework is applied to numerical reasoning tasks
The excerpt discusses a framework called Reasoning via Planning (RAP) that enables language models to reason and plan strategically. The framework uses a world model and Monte Carlo Tree Search to simulate states of the world and anticipate action outcomes. It bridges the gap between
This text excerpt includes a list of references to various papers and books related to language model planning and world models. Some key points include the use of model predictive control, integrated architectures for planning and learning, scaling language modeling, training verifiers for math word
This document provides a list of references related to reasoning with language models and planning with world models. The references include papers on various topics such as maieutic prompting, bandit-based Monte-Carlo planning, large language models as zero-shot reasoners
The summary discusses various research papers related to language models and planning. It includes references to papers on topics like self-play reinforcement learning algorithms, generating robot task plans using language models, cognitive maps in rats and men, learning world models and planning with neural systems