LLM-BABYBENCH: Understanding and Evaluating Grounded Planning and Reasoning in LLMs (2505.12135v1)

Abstract: Assessing the capacity of LLMs to plan and reason within the constraints of interactive environments is crucial for developing capable AI agents. We introduce $\textbf{LLM-BabyBench}$, a new benchmark suite designed specifically for this purpose. Built upon a textual adaptation of the procedurally generated BabyAI grid world, this suite evaluates LLMs on three fundamental aspects of grounded intelligence: (1) predicting the consequences of actions on the environment state ($\textbf{Predict}$ task), (2) generating sequences of low-level actions to achieve specified objectives ($\textbf{Plan}$ task), and (3) decomposing high-level instructions into coherent subgoal sequences ($\textbf{Decompose}$ task). We detail the methodology for generating the three corresponding datasets ($\texttt{LLM-BabyBench-Predict}$, $\texttt{-Plan}$, $\texttt{-Decompose}$) by extracting structured information from an expert agent operating within the text-based environment. Furthermore, we provide a standardized evaluation harness and metrics, including environment interaction for validating generated plans, to facilitate reproducible assessment of diverse LLMs. Initial baseline results highlight the challenges posed by these grounded reasoning tasks. The benchmark suite, datasets, data generation code, and evaluation code are made publicly available ($\href{https://github.com/choukrani/LLM-babybench}{\text{GitHub}}$, $\href{https://huggingface.co/datasets/salem-mbzuai/LLM-BabyBench}{\text{HuggingFace}}$).

Overview of "LLM-BabyBench: Understanding and Evaluating Grounded Planning and Reasoning in LLMs"

The paper "LLM-BabyBench: Understanding and Evaluating Grounded Planning and Reasoning in LLMs" introduces an innovative benchmark suite designed to evaluate the grounded reasoning capabilities of LLMs. This suite, termed LLM-BabyBench, focuses on assessing LLMs in interactive text-based environments, a crucial step in advancing AI towards effectively functioning as autonomous agents. LLM-BabyBench leverages the BabyAI platform—a procedurally generated grid world—to test models on three core tasks: predicting the consequences of actions (Predict task), crafting multi-step plans to achieve objectives (Plan task), and decomposing high-level instructions into achievable subgoals (Decompose task).

Methodology and Dataset Generation

The benchmark constructs are adapted from the BabyAI environment, which is based on the MiniGrid platform. BabyAI offers a controlled context of partially observable 2D grid-worlds, requiring navigation and interaction with various objects, articulated through "Baby Language." By adapting this framework, LLM-BabyBench provides a flexible interface enabling comprehensive evaluations on predefined tasks. The authors developed three structured datasets—LLM-BabyBench-Predict, -Plan, -Decompose—derived from expertly performed trajectories. These datasets are strategically designed to correspond to their respective evaluation tasks, thus enabling systematic assessment of LLMs' planning and reasoning abilities.

Evaluation and Baseline Findings

The evaluation mechanism of LLM-BabyBench consists of standardized metrics and an execution harness, ensuring interoperability across diverse LLM architectures. Key initial results demonstrate existing challenges, particularly in multi-step reasoning and spatial planning. The benchmark suite showed promise in highlighting performance discrepancies across different LLMs configurations, thereby providing a meaningful measure against baseline models.

Implications and Future Directions

LLM-BabyBench fills several gaps left by other LLM benchmarks, which traditionally focus on reasoning in symbolic or static domains detached from grounded realities. By introducing this benchmark, the authors address the lack of a standard method to rigorously test the grounding of LLMs' reasoning and planning capabilities in text-driven interactive settings.

In practical applications, this research holds significant potential for refining LLMs to operate effectively in real-time environments—critical for domains involving robotics, virtual assistants, and autonomous systems. The clear-cut evaluation framework and dataset accessibility offered by LLM-BabyBench provide an invaluable tool for advancing grounded AI research.

Conclusion

The development of LLM-BabyBench marks progress in the field of AI by establishing a benchmark that transcends traditional textual reasoning tasks, pushing the boundaries of what can be achieved in LLM development. This work not only highlights the current limitations of LLMs in engaging with grounded tasks but also sets the stage for future explorations into more complex, real-world challenges. By enabling direct comparisons of diverse models' grounded reasoning abilities, LLM-BabyBench is poised to become a cornerstone resource for researchers aiming to develop and analyze the next generation of AI systems.