Summary of Solving the Inverse Alignment Problem For Efficient Rlhf, by Shambhavi Krishna et al.
Solving the Inverse Alignment Problem for Efficient RLHF
by Shambhavi Krishna, Aishwarya Sahoo
First submitted to arxiv on: 13 Dec 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 The paper presents a novel approach to collecting high-quality preference datasets for reinforcement learning from human feedback (RLHF). The authors propose solving the “inverse alignment problem” in language model training, where they optimize the critic’s reward for a fixed actor and offline preference dataset. They fine-tune a reward model on subsets of the dataset aligned with a periodically frozen policy during RLHF. The results show that this approach leads to superior alignment and faster convergence compared to traditional methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about how to make machines learn from people’s feedback better. Right now, it’s hard to get good data for training reward models, so researchers often use old data that was collected in different ways. The authors think that this can make the reward model scores average out and not give clear feedback to the machine. They propose a new approach called “inverse alignment” to fix this problem. They fine-tune the reward model on smaller parts of the data that are related to what the machine is doing, and it works better than usual. |
Keywords
» Artificial intelligence » Alignment » Language model » Reinforcement learning from human feedback » Rlhf
