Summary of Gdpo: Learning to Directly Align Language Models with Diversity Using Gflownets, by Oh Joon Kwon et al.
GDPO: Learning to Directly Align Language Models with Diversity Using GFlowNets
by Oh Joon Kwon, Daiki E. Matsunaga, Kee-Eung Kim
First submitted to arxiv on: 19 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 novel approach to language models’ preference alignment is proposed, which aims to control their behavior to meet human needs and values. The study focuses on Reinforcement Learning with Human Feedback (RLHF) and its offline variant Direct Preference Optimization (DPO), both of which optimize a reward model based on human preferences. However, DPO is prone to overfitting the reward signals and generating suboptimal responses that may contain human biases in the dataset. To address this issue, a diversity-seeking RL algorithm called GFlowNet-DPO (GDPO) is proposed for offline preference alignment. The results demonstrate that GDPO can generate more diverse responses than baseline methods while still being aligned with human values in dialog generation and summarization tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Language models are getting better at understanding what we want, but they’re not always nice to us. Researchers want them to be nicer by giving them instructions on how to behave. They tried a method called Direct Preference Optimization (DPO) that makes the model listen to people’s preferences, but it has some problems. DPO gets too good at following the rules and forgets to be creative or respectful. To fix this, scientists created GFlowNet-DPO (GDPO), an algorithm that helps the model come up with more diverse responses while still being nice. |
Keywords
» Artificial intelligence » Alignment » Optimization » Overfitting » Reinforcement learning » Rlhf » Summarization
