Summary of Exploratory Preference Optimization: Harnessing Implicit Q*-approximation For Sample-efficient Rlhf, by Tengyang Xie et al.
Exploratory Preference Optimization: Harnessing Implicit Q*-Approximation for Sample-Efficient RLHF
by Tengyang Xie, Dylan J. Foster, Akshay Krishnamurthy, Corby Rosset, Ahmed Awadallah, Alexander Rakhlin
First submitted to arxiv on: 31 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (stat.ML)
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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 Reinforcement learning from human feedback (RLHF) has become crucial for language model alignment. This paper explores online exploration in RLHF, which leverages interactive access to human or AI feedback by intentionally producing diverse responses. Online exploration enables novel capabilities, but its full potential is hindered by computational and statistical bottlenecks. The proposed algorithm, Exploratory Preference Optimization (XPO), combines the Direct Preference Optimization (DPO) objective with a new exploration bonus, empowering the model to explore outside initial model and feedback data. XPO has provable guarantees and promising empirical performance. In theory, it is sample-efficient and converges to an optimal language model policy under natural exploration conditions. Empirically, XPO outperforms non-exploratory DPO variants in a preliminary evaluation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using computers to learn from people’s feedback on what they like or dislike. The goal is to make language models more helpful and honest. Right now, these models are mostly trained on data and can be biased. To fix this, the researchers developed a new way for the model to explore different responses based on human feedback. This allows the model to learn from people’s preferences and produce more diverse and accurate answers. The new approach is called Exploratory Preference Optimization (XPO) and it has shown promising results in initial tests. |
Keywords
» Artificial intelligence » Alignment » Language model » Optimization » Reinforcement learning from human feedback » Rlhf
