Summary of Transferable Post-training Via Inverse Value Learning, by Xinyu Lu et al.
Transferable Post-training via Inverse Value Learning
by Xinyu Lu, Xueru Wen, Yaojie Lu, Bowen Yu, Hongyu Lin, Haiyang Yu, Le Sun, Xianpei Han, Yongbin Li
First submitted to arxiv on: 28 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 proposes a novel approach to post-training processes, which are becoming increasingly complex due to larger datasets and growing base models. The authors introduce a separate neural network, dubbed the “value network,” that can be trained on a small base model using demonstrations and then integrated with other pre-trained models during inference. This enables them to achieve similar capability enhancements. The paper investigates best practices for this paradigm, including pre-training weights and connection schemes, and demonstrates its broad transferability across different pre-trained models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making it easier to improve the performance of artificial intelligence models. Right now, these improvements require a lot of data and computing power. The authors suggest a new way to do this using a smaller “value network” that can be trained quickly on some sample data. This value network can then be used with other pre-trained models to make them better without needing as much data or computing power. The paper shows that this approach works well across different types of AI models and even helps them perform similarly to full fine-tuning. |
Keywords
» Artificial intelligence » Fine tuning » Inference » Neural network » Transferability
