Chasing Progress, Not Perfection: Revisiting Strategies for End-to-End LLM Plan Generation

• URL: http://arxiv.org/abs/2412.10675v1
• Date: Sat, 14 Dec 2024 04:23:14 GMT
• Title: Chasing Progress, Not Perfection: Revisiting Strategies for End-to-End LLM Plan Generation
• Authors: Sukai Huang, Trevor Cohn, Nir Lipovetzky,
• Abstract summary: This study reassesses recent strategies by developing an end-to-end LLM planner.<n>We find that fine-tuning LLMs on a corpus of planning instances does not lead to robust planning skills.<n>Various strategies, including Chain-of-Thought, do enhance the probability of a plan being executable.
• Score: 34.636688162807836
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The capability of Large Language Models (LLMs) to plan remains a topic of debate. Some critics argue that strategies to boost LLMs' reasoning skills are ineffective in planning tasks, while others report strong outcomes merely from training models on a planning corpus. This study reassesses recent strategies by developing an end-to-end LLM planner and employing diverse metrics for a thorough evaluation. We find that merely fine-tuning LLMs on a corpus of planning instances does not lead to robust planning skills, as indicated by poor performance on out-of-distribution test sets. At the same time, we find that various strategies, including Chain-of-Thought, do enhance the probability of a plan being executable. This indicates progress towards better plan quality, despite not directly enhancing the final validity rate. Among the strategies we evaluated, reinforcement learning with our novel `Longest Contiguous Common Subsequence' reward emerged as the most effective, contributing to both plan validity and executability. Overall, our research addresses key misconceptions in the LLM-planning literature; we validate incremental progress in plan executability, although plan validity remains a challenge. Hence, future strategies should focus on both these aspects, drawing insights from our findings.

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