Summary of Chasing Progress, Not Perfection: Revisiting Strategies For End-to-end Llm Plan Generation, by Sukai Huang et al.
Chasing Progress, Not Perfection: Revisiting Strategies for End-to-End LLM Plan Generation
by Sukai Huang, Trevor Cohn, Nir Lipovetzky
First submitted to arxiv on: 14 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study reevaluates recent approaches to training Large Language Models (LLMs) for planning tasks. A new end-to-end LLM planner is developed and tested using various metrics. The results show that simply fine-tuning LLMs on a planning corpus does not lead to robust planning skills, but strategies like Chain-of-Thought can improve the probability of a plan being executable. Reinforcement learning with a novel reward function, Longest Contiguous Common Subsequence, is found to be the most effective approach, enhancing both plan validity and executability. The study highlights key misconceptions in the LLM-planning literature and suggests that future strategies should focus on improving both plan validity and executability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research investigates whether Large Language Models (LLMs) can truly plan. Some people think that training these models is enough, but others believe it’s not enough to get good results. The researchers tested different ways to train LLMs and found that just fine-tuning them wasn’t effective. However, they did find that some strategies improved the chances of a plan being good. A new way to train LLMs using reinforcement learning was the most successful. This study shows what works and what doesn’t in training LLMs for planning tasks. |
Keywords
» Artificial intelligence » Fine tuning » Probability » Reinforcement learning
