Summary of Codeit: Self-improving Language Models with Prioritized Hindsight Replay, by Natasha Butt et al.
CodeIt: Self-Improving Language Models with Prioritized Hindsight Replay
by Natasha Butt, Blazej Manczak, Auke Wiggers, Corrado Rainone, David W. Zhang, Michaël Defferrard, Taco Cohen
First submitted to arxiv on: 7 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); 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 This paper tackles the challenge of large language models’ limited ability to solve tasks that require human-level reasoning. While they excel in specific areas, they struggle on general intelligence benchmarks like the Abstraction and Reasoning Corpus (ARC). The authors propose a novel method called Code Iteration (CodeIt) for self-improvement of language models. CodeIt involves iterative learning from prioritized experience replay and hindsight relabeling. This approach addresses the sparse rewards in program synthesis, allowing for successful inter-task generalization on the ARC dataset. By combining pre-training, data-augmentation, and CodeIt, the authors achieve state-of-the-art performance, outperforming existing neural and symbolic baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how computers can learn to solve complex problems. Right now, super smart language models are good at doing specific tasks, but they’re really bad at figuring things out like humans do. The researchers came up with a new way to improve these models called CodeIt. It’s like teaching the model by showing it examples and saying “aha! that’s what I meant!” This helps the model learn from its mistakes and get better at solving problems. They tested this method on a big challenge called ARC, and it worked really well! Now we can use this new method to make computers even smarter. |
Keywords
* Artificial intelligence * Data augmentation * Generalization
