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@@ -37,4 +37,4 @@ Highlighting top ML papers of the week.
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| 7. This work aims to better understand how deep learning models overfit or memorize examples; interesting phenomena observed; important work toward a mechanistic theory of memorization. | [Paper](https://transformer-circuits.pub/2023/toy-double-descent/index.html) |
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| 8. StitchNet: Interesting idea to create new coherent neural networks by reusing pretrained fragments of existing NNs. Not straightforward but there is potential in terms of efficiently reusing learned knowledge in pre-trained networks for complex tasks. | [Paper](https://arxiv.org/abs/2301.01947) |
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| 9. Proposes integrated decomposition, an approach to improve Science Q&A through a human-in-the-loop workflow for refining compositional LM programs. | [Paper](https://arxiv.org/abs/2301.01751) |
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-| 10. A Succinct Summary of Reinforcement Learning. A nice little overview of some important ideas in RL. | [Content Cell](https://arxiv.org/abs/2301.01379) |
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+| 10. A Succinct Summary of Reinforcement Learning. A nice little overview of some important ideas in RL. | [Paper](https://arxiv.org/abs/2301.01379) |
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