Summary of Improving Language Model Reasoning with Self-motivated Learning, by Yunlong Feng et al.
Improving Language Model Reasoning with Self-motivated Learning
by Yunlong Feng, Yang Xu, Libo Qin, Yasheng Wang, Wanxiang Che
First submitted to arxiv on: 10 Apr 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 A new framework called Self-motivated Learning is proposed to improve model performance by automatically generating rationales. This is done by training a reward model to evaluate rationale quality and using reinforcement learning to improve reasoning capability. The framework motivates the model itself to generate better rationales on existing datasets, leading to higher reasoning ability. Experimental results on multiple reasoning datasets show that this method significantly improves model performance, even outperforming text-davinci-002 in some cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Self-motivated Learning framework helps models learn by themselves to come up with good reasons for their answers. This is important because it’s hard to find lots of data with good reasons written down. To do this, the model uses a special reward system that says how good its reasons are, and then uses this to get better at coming up with good reasons. The results show that this helps models get much better at making sense of things. |
Keywords
» Artificial intelligence » Reinforcement learning
