BAR : A Backward Reasoning based Agent for Complex M inecraft Tasks (2025)

Abstract: Large language model (LLM) based agents have shown great potential in following human instructions and automatically completing various tasks. To complete a task, the agent needs to decompose it into easily executed steps by planning. Existing studies mainly conduct the planning by inferring what steps should be executed next starting from the agent’s initial state. However, this forward reasoning paradigm doesn’t work well for complex tasks. We propose to study this issue in Minecraft, a virtual environment that simulates complex tasks based on real-world scenarios. We believe that the failure of forward reasoning is caused by the big perception gap between the agent’s initial state and task goal. To this end, we leverage backward reasoning and make the planning starting from the terminal state, which can directly achieve the task goal in one step. Specifically, we design a backward reasoning based agent (BAR). It is equipped with a recursive goal decomposition module, a state consistency maintaining module and a stage memory module to make robust, consistent, and efficient planning starting from the terminal state. Experimental results demonstrate the superiority of BAR over existing methods and the effectiveness of proposed modules.

## introduction large language model (llm) based agents are a powerful tool to support human-machine intelligence because of their potential to follow human instructions. however, it is not possible to complete one complex task in a single step. the key lies * corresponding author in how to decompose the task into easily executed steps. existing llm-based approaches mostly focus on forward reasoning based planning @xcite @xcite . they infer what steps should be executed next starting from the agent's initial state, where no steps have yet been executed. however, we observe this reasoning paradigm doesn't work well for complex tasks. let us take a close look at a complex task (obtain a diamond pickaxe) in an open-world environment, minecraft, which simulates numerous complex tasks based on real-world scenarios. as shown in the upper part of figure 1 , the forward reasoning agent fails to infer the correct next step after the step "craft 8 (stick)". this is because there is a huge perception gap between the agent's initial state and the task goal. to tackle this issue, we propose to use backward reasoning @xcite , another powerful reasoning paradigm foot_0 . as shown in the lower part of figure 1 , by backward reasoning, the agent infers the steps to be executed starting from the terminal state. in the terminal state, the task goal has been achieved and the agent has already obtained 1 (diamond pickaxe). to achieve this goal, it is easy to infer that the last step should be "craft 1 " because after executing this step the agent can obtain 1 diamond pickaxe. furthermore, to successfully execute the step "craft 1 ", the agent needs to prepare the materials for it. so the second to last step should be "mine 3 (diamond)" as is the figure 2 : the conflict of the agent's physical states during planning. in minecraft, (stone) can only be obtained below the ground, so it is necessary to reach below the ground first and then mine (stone). best viewed on screen. despite this advantage, backward reasoning is not a perfect solution -the agent only considers the current goal to be achieved, ignoring the current world state. as shown in figure 2 , the agent infers the step "mine (stone)" immediately after inserting the step "craft 1 (wooden pickaxe)". however, this step cannot be executed successfully because (stone) can only be obtained below the ground but the agent was above the ground before executing this step. at last, this conflict results in a failing planning for the task. to this end, we propose bar, a backward reasoning based agent for complex minecraft tasks, which is equipped with three carefullydesigned modules. (1) inspired by the dynamic programming algorithm @xcite , we design the recursive goal decomposition module to support robust backward planning starting from the terminal state. (2) we develop the state consistency maintaining module to monitor the agent's states and handle any state conflicts during planning. (3) we introduce the stage memory module to make the best of existing interactions and promote more efficient planning. experimental results show that the bar agent outperforms other sota methods by a large margin. we hope our exploration and discoveries can promote the development of more efficient llm-based planning agents. in summary, our contributions are three-fold: ## method our agent bar consists of three key modules: (a) recursive goal decomposition module; (b) state consistency maintaining module; (c) stage memory module. the first two modules are designed to provide robust and consistent planning, and the third module is designed to further improve planning efficiency by interacting with the environment. ## experiments to evaluate the effectiveness of our proposed agent bar, we conduct extensive experiments to compare the planning performance of bar with current sota baselines, on both static and dynamic planning settings. ## conclusion in this paper, we study the planning problem for complex tasks in the minecraft environment. we argue that, compared with forward reasoning, backward reasoning has the advantage to avoid the perception gap between the agent's initial state and the task goal. we further point out a key weakness of backward reasoning is that it may fail to complete the task because of the state conflicts during planning. to this end, we propose bar with: (1) recursive goal decomposition to make robust backward reasoning, (2) a state consistency maintaining module to address state conflicts, (3) and a stage memory module promote more efficient planning. experimental results show the superiority of our agent in both static and dynamic planning, surpassing other baselines by a large margin.