Summary of Reward Model Learning Vs. Direct Policy Optimization: a Comparative Analysis Of Learning From Human Preferences, by Andi Nika et al.
Reward Model Learning vs. Direct Policy Optimization: A Comparative Analysis of Learning from Human Preferences
by Andi Nika, Debmalya Mandal, Parameswaran Kamalaruban, Georgios Tzannetos, Goran Radanović, Adish Singla
First submitted to arxiv on: 4 Mar 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 compares two approaches in reinforcement learning from human feedback (RLHF) and direct preference optimization (DPO), focusing on loglinear policy parametrization and linear reward functions. The authors derive statistical bounds on the suboptimality gap for both paradigms, assuming an oracle that solves optimization problems exactly. They discuss the relative comparison between RLHF and DPO, considering sample size, policy and reward class dimensions, and regularization temperature. Additionally, they analyze approximate optimization settings and derive exponentially decaying convergence rates. The authors also explore scenarios where the ground-truth reward is not realizable, showing that DPO retains its asymptotically decaying gap by tuning the temperature. Finally, they generalize their results to Markov decision processes. This study provides a comprehensive comparison of RLHF and DPO. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper compares two ways that computers learn from what people like or dislike. The first way is called reinforcement learning from human feedback (RLHF), where the computer tries different actions and gets feedback on whether they are good or bad. The second way is direct preference optimization (DPO), where the computer directly asks for people’s preferences. The researchers compare these two ways, looking at how well they work in different situations. They found that one method works better than the other under certain conditions. This study helps us understand which method to use in different situations. |
Keywords
* Artificial intelligence * Optimization * Regularization * Reinforcement learning from human feedback * Rlhf * Temperature
