Summary of Optimal Design For Reward Modeling in Rlhf, by Antoine Scheid et al.
Optimal Design for Reward Modeling in RLHF
by Antoine Scheid, Etienne Boursier, Alain Durmus, Michael I. Jordan, Pierre Ménard, Eric Moulines, Michal Valko
First submitted to arxiv on: 22 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 A reinforcement learning framework for aligning language models with human preferences using pairwise text generation preferences is explored in this paper. The authors focus on the costly process of collecting these preferences and propose a linear contextual dueling bandit method to select an effective dataset. They frame the problem as a simple regret minimization task and develop an offline framework for solving it, providing bounds on the simple regret under certain assumptions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper aims to formalize the reward training model in RLHF by collecting human pairwise preferences across various text generations and using them to infer a reward model. The authors propose a linear contextual dueling bandit method to select an effective dataset and frame the problem as a simple regret minimization task. They also develop an offline framework for solving it, providing bounds on the simple regret under certain assumptions. |
Keywords
» Artificial intelligence » Reinforcement learning » Rlhf » Text generation
