Summary of Adaptive Preference Scaling For Reinforcement Learning with Human Feedback, by Ilgee Hong et al.
Adaptive Preference Scaling for Reinforcement Learning with Human Feedback
by Ilgee Hong, Zichong Li, Alexander Bukharin, Yixiao Li, Haoming Jiang, Tianbao Yang, Tuo Zhao
First submitted to arxiv on: 4 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel adaptive preference loss for reinforcement learning from human feedback (RLHF), which addresses uncertainty in preference strength by incorporating an adaptive scaling parameter. The loss function is designed to capture varying strengths of preferences across different pairs and is computationally efficient, enabling its optimization through a simple second-order algorithm. The proposed method improves policy performance and aligns reward function selection more closely with policy optimization, simplifying the hyperparameter tuning process. The approach is versatile and can be adapted to various preference optimization frameworks, including direct preference optimization (DPO). Experiments with robotic control and natural language generation with large language models (LLMs) demonstrate the effectiveness of the proposed method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a problem in AI alignment by making rewards more flexible. Right now, AI systems are not aligned well with human values because they don’t know how much humans prefer different things. The authors propose a new way to learn from human feedback that takes this uncertainty into account. Their approach is called “adaptive preference loss” and it works by adjusting the importance of each piece of feedback based on how clear or unclear it is. This makes the AI system more likely to follow what humans really want, not just what they say in a given situation. The authors tested their method with robots and language models and showed that it improves performance and makes it easier to set up the AI system. |
Keywords
» Artificial intelligence » Alignment » Hyperparameter » Loss function » Optimization » Reinforcement learning from human feedback » Rlhf
