Query-Efficient Planning with Language Models (2412.06162v1)

Abstract: Planning in complex environments requires an agent to efficiently query a world model to find a feasible sequence of actions from start to goal. Recent work has shown that LLMs, with their rich prior knowledge and reasoning capabilities, can potentially help with planning by searching over promising states and adapting to feedback from the world. In this paper, we propose and study two fundamentally competing frameworks that leverage LLMs for query-efficient planning. The first uses LLMs as a heuristic within a search-based planner to select promising nodes to expand and propose promising actions. The second uses LLMs as a generative planner to propose an entire sequence of actions from start to goal, query a world model, and adapt based on feedback. We show that while both approaches improve upon comparable baselines, using an LLM as a generative planner results in significantly fewer interactions. Our key finding is that the LLM as a planner can more rapidly adapt its planning strategies based on immediate feedback than LLM as a heuristic. We present evaluations and ablations on Robotouille and PDDL planning benchmarks and discuss connections to existing theory on query-efficient planning algorithms. Code is available at https://github.com/portal-cornell/LLMs-for-planning

Query-Efficient Planning with LLMs: An Expert Summary

The research document entitled "Query-Efficient Planning with LLMs" investigates the role of LLMs in enhancing query-efficiency in planning domains. Planning is a significant facet of AI and robotics, focusing on determining a sequence of steps to transition an agent from a starting state to a target state within a complex environment. The central challenge in such domains often lies in the expense associated with querying a world model that simulates the outcome of actions within the environment.

Core Contributions

The paper presents two primary frameworks that leverage LLMs in planning tasks:

1. LLM as a Heuristic: This approach integrates LLMs into heuristic-based planners. LLMs are utilized to select promising states for expansion within a search tree, thereby focusing computational resources on fruitful areas of the search space. This integration is akin to using traditional heuristic functions in search algorithms like A* or Dijkstra's algorithm but employs the nuanced, contextual understanding of LLMs to guide the search process more intelligently.
2. LLM as a Generative Planner: Here, LLMs take on a more autonomous role by generating entire sequences of actions. Following the generation of a proposed plan, these models query a world model for validation. The critical observation is that this method relocates the process of decision-making to the generative capacities of LLMs, allowing for swift adaptations to immediate feedback.

Notably, the paper shows that while both frameworks surpass conventional baselines, the generative planner demonstrates superior performance regarding query-efficiency. This distinction emerges because generative planners can adjust their entire plan dynamically in response to feedback, whereas heuristic LLMs are limited by the nodes selected by the underlying planner.

Numerical Evaluations and Theoretical Implications

The research documents a significant reduction in interaction requirements with world models when using LLMs as generative planners. Through a range of experimental evaluations on Robotouille—a robotics simulator used for cooking tasks—and PDDL (Planning Domain Definition Language) benchmarks, the paper illustrates tangible advancements in planning efficiency. Specifically, it reports substantial improvements in success rates and reduced average query counts across various domains including Blocksworld, Logistics, and Grippers.

The implications of this work are two-fold:

• Practical Perspectives: By reducing the number of interactions required, these approaches make planning feasible in environments where real-time querying is costly. This has pronounced applications in robotics, where decision-making speed and computational resource usage are pivotal.
• Theoretical Perspectives: The integration of LLMs as generative planners offers a promising avenue for exploring query-efficient adaptations of lazy search techniques in the planning literature. This approach ties into theories of posterior sampling and Bayesian reinforcement learning, providing a conceptual framework that could be extended to other AI-driven learning problems.

Speculation on Future AI Developments

The insights presented in this paper pave the way for further advancements in AI research, particularly in the intersection of LLMs and planning algorithms. Possible future directions include:

• Enhancing Scalability: Further research could explore improving these frameworks to handle longer planning horizons and more intricate environments, such as task and motion planning scenarios involving complicated geometric reasoning.
• Hybrid Systems: Future work might also investigate hybrid systems that combine the heuristic guidance of LLMs with the robustness of traditional planners. This could potentially harness the strengths of both approaches to create even more efficient planning algorithms.

In conclusion, this paper delivers compelling evidence that LLMs can foster dramatic improvements in the field of query-efficient planning. The dual approaches of using LLMs as both heuristic tools and generative planners offer broad potential to revolutionize traditional methods used in AI planning and robotics, suggesting a transformative impact on how complex decision-making processes are approached in AI research.