Summary of Smart: Self-learning Meta-strategy Agent For Reasoning Tasks, by Rongxing Liu et al.
SMART: Self-learning Meta-strategy Agent for Reasoning Tasks
by Rongxing Liu, Kumar Shridhar, Manish Prajapat, Patrick Xia, Mrinmaya Sachan
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 The proposed framework, SMART (Self-learning Meta-strategy Agent for Reasoning Tasks), enables Language Models (LMs) to autonomously learn and select the most effective strategies for various reasoning tasks. The strategy selection process is modeled as a Markov Decision Process and leverages reinforcement learning-driven continuous self-improvement. This allows LMs to internalize the outcomes of their own reasoning processes and adjust their strategy accordingly, aiming for correct solutions on the first attempt. Experimental results across various reasoning datasets and with different model architectures demonstrate that SMART significantly enhances the ability of models to choose optimal strategies without external guidance, achieving higher accuracy with a single inference pass. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new framework called SMART that helps Language Models make better decisions when solving problems. Right now, these models often have to try different approaches before finding the right one. The researchers asked: Can we teach these models to pick the best approach from the start? They developed a way for the model to learn and improve its strategy based on how well it does. This means the model can get better at making decisions without needing multiple tries or external help. The results show that this new method can lead to more accurate answers with less effort, which is exciting for future applications of Language Models. |
Keywords
» Artificial intelligence » Inference » Reinforcement learning
