Summary Language Agent Tree Search Unifies Reasoning arxiv.org
11,983 words - PDF document - View PDF document
One Line
LATS integrates planning, acting, and reasoning in language models to improve decision-making through external feedback and self-reflection.
Slides
Slide Presentation (8 slides)
Key Points
- LATS is a framework that combines planning, acting, and reasoning capabilities in large language models (LLMs) to enhance decision-making.
- LATS utilizes an external environment for feedback, allowing for more deliberate and adaptive decision-making.
- LATS adapts Monte Carlo tree search (MCTS) to language agents and incorporates external feedback to enhance reasoning.
- LATS achieves high performance on HumanEval for programming and on HotPotQA.
- LATS addresses the limitations of existing techniques by incorporating external feedback and self-reflection.
Summaries
19 word summary
LATS combines planning, acting, and reasoning in large language models to enhance decision-making by incorporating external feedback and self-reflection.
58 word summary
LATS (Language Agent Tree Search) combines planning, acting, and reasoning in large language models (LLMs) to enhance decision-making. It addresses limitations by incorporating external feedback and self-reflection. LATS unifies LM planning, acting, and reasoning strategies, adapting Monte Carlo tree search (MCTS) to language agents. Experimental evaluations demonstrate LATS's applicability and effectiveness in diverse domains, improving decision-making in LLMs.
127 word summary
LATS (Language Agent Tree Search) is a framework that combines planning, acting, and reasoning capabilities in large language models (LLMs) to enhance decision-making. LATS addresses the limitations of existing techniques by incorporating external feedback and self-reflection. LLMs offer strong reasoning and general adaptability, making them an alternative paradigm to traditional reinforcement learning. However, LLMs often lack deliberate decision-making characteristics. To overcome this, LATS is introduced as a framework that unifies LM planning, acting, and reasoning strategies. It adapts Monte Carlo tree search (MCTS) from model-based reinforcement learning to language agents and utilizes text as an interface for adaptive planning. Experimental evaluations across diverse domains demonstrate the applicability and effectiveness of LATS for both reasoning and acting tasks. LATS is a versatile framework that improves decision-making in LLMs.
445 word summary
LATS (Language Agent Tree Search) is a framework that combines planning, acting, and reasoning capabilities in large language models (LLMs) to enhance decision-making. By incorporating external feedback and self-reflection, LATS addresses the limitations of existing techniques. It utilizes LLMs as agents, value functions, and optimizers, allowing for more deliberate and adaptive decision-making beyond traditional methods.
While traditional reinforcement learning has focused on developing autonomous agents capable of reasoning and decision-making, LLMs offer an alternative paradigm that provides strong reasoning and general adaptability. LLMs have excelled in natural language processing tasks and have been successful in complex environments involving knowledge and reasoning.
However, LLMs often lack deliberate and thoughtful decision-making characteristics. Existing methods fail to consider multiple reasoning paths or plan ahead. To overcome these limitations, LATS is introduced as a general framework for decision-making and reasoning with language models. It expands on existing methods like CoT and ReAct by unifying LM planning, acting, and reasoning strategies. LATS adapts Monte Carlo tree search (MCTS) from model-based reinforcement learning to language agents, using the LM as an agent, value function, and optimizer. It utilizes text as an interface between components for adaptive planning without additional training.
Experimental evaluations across diverse domains demonstrate the applicability of LATS for both reasoning and acting tasks. It achieves high performance on HumanEval for programming with GPT-4 and on HotPotQA with GPT-3.5, showcasing its effectiveness and generality.
LLMs have been used for reasoning tasks by decomposing complex inputs into sequential intermediate steps. Various prompting techniques have been developed to improve LM performance, but error propagation remains an issue. Recent advancements like ToT prompting and RAP aim to mitigate this issue by exploring multiple reasoning paths.
In addition, LLMs have been adapted for decision-making tasks in robotics and complex games. Acting-based prompting techniques, such as ReAct, have shown success in text-based environments but lack planning capabilities.
LATS employs tree-based search, a widely used technique in planning algorithms and reinforcement learning, to fully utilize the potential of LLMs. It selects nodes, expands the tree with sampled actions, evaluates the nodes based on their values, simulates trajectories until a terminal state is reached, and backpropagates the resulting values. Self-reflection is used to refine the decision-making process.
In conclusion, LATS is a framework that unifies planning, acting, and reasoning in large language models for enhanced decision-making. It incorporates external feedback and self-reflection to address existing limitations. Experimental evaluations demonstrate its versatility and effectiveness across various domains. The appendix provides additional details about the LATS algorithm, including pseudocode, further discussion, limitations, future directions, and the broader impact. It also specifies the details of the environments used in the experiments, demonstrating the application of LATS in different scenarios.
565 word summary
LATS (Language Agent Tree Search) is a framework that enhances decision-making by combining planning, acting, and reasoning capabilities in large language models (LLMs). It addresses the limitations of existing techniques by incorporating external feedback and self-reflection. LATS utilizes LLMs as agents, value functions, and optimizers, repurposing their strengths for improved problem-solving. The framework employs an external environment for feedback, allowing for more deliberate and adaptive decision-making beyond the limitations of existing techniques.
Traditional reinforcement learning has focused on developing autonomous agents capable of reasoning and decision-making. However, the rise of large language models (LLMs) offers an alternative paradigm that provides strong reasoning and general adaptability. LLMs have excelled in natural language processing tasks and have been adapted to a wide range of tasks that require advanced common-sense reasoning or quantitative skills. They have also been successful in complex environments that involve knowledge and reasoning.
While LLMs have shown promise in reasoning and acting tasks, they often lack deliberate and thoughtful decision-making characteristics. Existing methods fail to consider multiple reasoning paths or plan ahead. To address these limitations, LATS is introduced as a general framework for decision-making and reasoning with language models. It unifies LM planning, acting, and reasoning strategies by expanding on existing methods like CoT and ReAct. LATS adapts Monte Carlo tree search (MCTS) from model-based reinforcement learning to language agents, using the LM as an agent, value function, and optimizer. Text is used as an interface between components, allowing for adaptive planning without additional training.
Experimental evaluations across diverse domains demonstrate the applicability of LATS for both reasoning and acting tasks. LATS achieves high performance on HumanEval for programming with GPT-4 and on HotPotQA with GPT-3.5. The effectiveness and generality of LATS are demonstrated through these experiments.
LLMs have been used for reasoning tasks by decomposing complex inputs into sequential intermediate steps. Various prompting techniques have been developed to improve LM performance, including Chain-of-Thought (CoT) prompting and its variants. However, these methods often suffer from error propagation as the number of steps increases. Recent advancements, such as ToT prompting and Reasoning via Planning (RAP), have aimed to mitigate this issue by exploring multiple reasoning paths.
LLMs have also been adapted for decision-making tasks in robotics and complex games. Acting-based prompting techniques, such as ReAct, have seen success in text-based environments. These methods have been enhanced by access to external tools or models but lack planning capabilities.
LATS employs tree-based search, a widely used technique in planning algorithms and reinforcement learning, to fully utilize the potential of LLMs. The search algorithm selects nodes, expands the tree with sampled actions, evaluates the nodes based on their values, simulates trajectories until a terminal state is reached, and backpropagates the resulting values. Self-reflection is used to refine the decision-making process.
In conclusion, LATS is a framework that unifies planning, acting, and reasoning in large language models for enhanced decision-making. It addresses the limitations of existing techniques by incorporating external feedback and self-reflection. Experimental evaluations demonstrate the versatility and effectiveness of LATS across various domains. The appendix provides additional details about the LATS algorithm, including the pseudocode, further discussion on the method and its limitations, future directions, and the broader impact. The appendix also specifies the details of the environments used in the experiments, including the HotPotQA, Programming, and Webshop environments. It lists the prompts used for each environment, demonstrating how LATS can be applied in different scenarios.
1020 word summary
LATS (Language Agent Tree Search) is a framework that combines planning, acting, and reasoning capabilities in large language models (LLMs) to enhance decision-making. It employs LLMs as agents, value functions, and optimizers, repurposing their strengths for improved problem-solving. LATS utilizes an external environment for feedback, allowing for more deliberate and adaptive decision-making beyond the limitations of existing techniques.
In traditional reinforcement learning, general autonomous agents capable of reasoning and decision-making have been of interest. However, with the rise of large language models (LLMs), there is an alternative paradigm that offers strong reasoning and general adaptability. LLMs have excelled in natural language processing tasks and have been adapted to a wide range of tasks that require advanced common-sense reasoning or quantitative skills. They have also been successful in complex environments that involve knowledge and reasoning.
While LLMs have shown promise in reasoning and acting tasks, they often lack the deliberate and thoughtful decision-making characteristics of humans. Existing methods fail to consider multiple reasoning paths or plan ahead. Recent search-guided LLM works have addressed this issue by searching over multiple chains of thought or using Monte Carlo tree search (MCTS). However, these methods do not incorporate external feedback that can enhance reasoning.
To address these limitations, LATS is introduced as a general framework for decision-making and reasoning with language models. It unifies LM planning, acting, and reasoning strategies by expanding on existing methods like CoT and ReAct. LATS adapts MCTS from model-based reinforcement learning to language agents, using the LM as an agent, value function, and optimizer. Text is used as an interface between components, allowing for adaptive planning without additional training.
Experimental evaluations across diverse domains such as programming, HotPotQA, and WebShop demonstrate the applicability of LATS for both reasoning and acting tasks. LATS achieves high performance on HumanEval for programming with GPT-4 and on HotPotQA with GPT-3.5. The effectiveness and generality of LATS are demonstrated through these experiments.
LLMs have been used for reasoning tasks by decomposing complex inputs into sequential intermediate steps. Various prompting techniques have been developed to improve LM performance, including Chain-of-Thought (CoT) prompting and its variants. However, these methods often suffer from error propagation as the number of steps increases. Recent advancements, such as ToT prompting and Reasoning via Planning (RAP), have aimed to mitigate this issue by exploring multiple reasoning paths.
LLMs have also been adapted for decision-making tasks in robotics and complex games. Acting-based prompting techniques, such as ReAct, have seen success in text-based environments. These methods have been enhanced by access to external tools or models but lack planning capabilities.
Tree-based search, a widely used technique in planning algorithms and reinforcement learning, is employed in LATS to fully utilize the potential of LLMs. The search algorithm selects nodes, expands the tree with sampled actions, evaluates the nodes based on their values, simulates trajectories until a terminal state is reached, and backpropagates the resulting values. Self-reflection is used to refine the decision-making process.
Experiments on HotPotQA, programming, and WebShop demonstrate the effectiveness of LATS. LATS outperforms existing methods in terms of reasoning and acting performance. The use of external feedback and self-reflection improves sensibility and adaptability. LATS achieves state-of-the-art performance on HumanEval and surpasses reinforcement learning-based approaches on WebShop.
In conclusion, LATS is a framework that unifies planning, acting, and reasoning in large language models for enhanced decision-making. It addresses the limitations of existing techniques by incorporating external feedback and self-reflection. Experimental evaluations demonstrate the versatility and effectiveness of LATS across various domains.
The Language Agent Tree Search (LATS) algorithm is proposed as a framework to improve reasoning and decision-making in large language models (LLMs). LATS combines thought, action, and observation steps to enhance the performance of LLMs in complex tasks. The algorithm uses a tree search approach to explore different actions and their consequences in an environment. It incorporates a value function to evaluate the potential outcomes of each action and a reflection generator to provide feedback on previous actions. LATS aims to generate high-level linguistic reasoning and actions through multiple rounds of decision-making and reflection.
The LATS algorithm is presented in pseudocode in the paper. It involves initializing the action and observation spaces, as well as the state-action value function and visit counter. The algorithm then iterates through a series of steps, including expansion and simulation, evaluation, selection, and backpropagation. During expansion and simulation, the algorithm generates multiple actions and observations based on the current state. The evaluation step calculates the value of each state-action pair using the value function. The selection step chooses the action with the highest value, taking into account the visit counter and exploration weight. Finally, the backpropagation step updates the state-action value function based on the rewards received.
The limitations of LATS are also discussed in the paper. The algorithm requires more computational resources and time compared to simpler techniques, making it less efficient. However, it is recommended for difficult tasks or situations where performance is prioritized over efficiency. The authors hope that future advancements in LLMs will reduce the computational costs associated with LATS.
The broader impact of LATS is also considered. While LATS improves autonomous decision-making, it also raises concerns about potential harmful uses of LLMs. However, LATS enhances interpretability and alignment by generating understandable high-level linguistic reasoning and actions. This is in contrast to relying on implicit low-level token values.
The appendix provides additional details about the LATS algorithm, including the pseudocode, further discussion on the method and its limitations, future directions, and the broader impact. The appendix also specifies the details of the environments used in the experiments, including the HotPotQA, Programming, and Webshop environments. It lists the prompts used for each environment, demonstrating how LATS can be applied in different scenarios.
Overall, the Language Agent Tree Search (LATS) algorithm is proposed as a framework to improve reasoning and decision-making in large language models. The algorithm combines thought, action, and observation steps to enhance performance in complex tasks. While LATS has limitations in terms of computational costs, it has the potential to improve interpretability and alignment in LLMs. The appendix provides additional details and prompts for different environments where LATS can be applied.