Unlocking the Planning Capabilities of Large Language Models with Maximum Diversity Fine-tuning (2025.findings-naacl)
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| Challenge: | Existing studies have shown that LLMs struggle to generate valid plans in the automated planning domain due to weak System 2 competencies. |
| Approach: | They propose a method which uses a random sampling method to select diverse and representative data to enhance sample efficiency and the model’s generalization capability. |
| Outcome: | The proposed method outperforms baseline methods across scales and multiple benchmark domains. |
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