Summary of Contrastive Policy Gradient: Aligning Llms on Sequence-level Scores in a Supervised-friendly Fashion, by Yannis Flet-berliac et al.
Contrastive Policy Gradient: Aligning LLMs on sequence-level scores in a supervised-friendly fashion
by Yannis Flet-Berliac, Nathan Grinsztajn, Florian Strub, Bill Wu, Eugene Choi, Chris Cremer, Arash Ahmadian, Yash Chandak, Mohammad Gheshlaghi Azar, Olivier Pietquin, Matthieu Geist
First submitted to arxiv on: 27 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 This paper introduces Contrastive Policy Gradient (CoPG), a new reinforcement learning algorithm that can estimate the optimal policy from off-policy data. CoPG is designed to address limitations in current methods, which often require on-policy or near-on-policy samples and are sensitive to important sampling techniques. The proposed approach highlights the importance of using a state baseline and generalizes previous direct alignment methods like IPO (identity preference optimization) and classic policy gradient. The authors demonstrate the effectiveness of CoPG on both a toy bandit problem and finetuning Large Language Models for summarization tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using a new way to teach machines how to make good choices. Right now, we use a method called reinforcement learning, which helps machines learn from rewards or punishments. The new approach, called CoPG, can learn from data that isn’t as specific, making it more efficient and flexible. This could help us train better language models for tasks like summarizing text. |
Keywords
» Artificial intelligence » Alignment » Optimization » Reinforcement learning » Summarization
