Summary of Reinforcement Learning From Human Feedback Without Reward Inference: Model-free Algorithm and Instance-dependent Analysis, by Qining Zhang et al.
Reinforcement Learning from Human Feedback without Reward Inference: Model-Free Algorithm and Instance-Dependent Analysis
by Qining Zhang, Honghao Wei, Lei Ying
First submitted to arxiv on: 11 Jun 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 This paper proposes a novel reinforcement learning from human feedback (RLHF) algorithm, called , which identifies the optimal policy directly from human preference information without explicit reward model inference. The algorithm employs a dueling bandit sub-routine and adaptive stopping criteria to efficiently explore the state space and identify the superior actions. The paper shows that RLHF has sample complexity similar to classic RL and can be transformed into an explore-then-commit algorithm with logarithmic regret. Additionally, the authors generalize their approach to discounted MDPs using a frame-based approach. The results demonstrate that end-to-end RLHF may deliver improved performance by avoiding pitfalls in reward inference such as overfitting and distribution shift. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to learn from human feedback, called reinforcement learning from human feedback (RLHF). Instead of trying to understand what rewards to give a model, the authors create an algorithm that learns directly from what humans prefer. They call this algorithm and show it can be used in situations where we don’t have a clear reward function. The paper also compares RLHF to traditional reinforcement learning and shows that they are similar in terms of how much data is needed to learn. Overall, the authors think that RLHF could be a powerful tool for training language models. |
Keywords
» Artificial intelligence » Inference » Overfitting » Reinforcement learning » Reinforcement learning from human feedback » Rlhf
