Summary of Rewards-in-context: Multi-objective Alignment Of Foundation Models with Dynamic Preference Adjustment, by Rui Yang et al.
Rewards-in-Context: Multi-objective Alignment of Foundation Models with Dynamic Preference Adjustment
by Rui Yang, Xiaoman Pan, Feng Luo, Shuang Qiu, Han Zhong, Dong Yu, Jianshu Chen
First submitted to arxiv on: 15 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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 This paper tackles the challenge of aligning foundation models with human preferences, a crucial step towards developing AI systems that are both helpful and harmless. The problem is made more complex by the need to fine-tune large models using reinforcement learning (RL), which can be costly and unstable. To address this issue, the authors propose Rewards-in-Context (RiC), a method that conditions the response of a foundation model on multiple rewards within its prompt context and applies supervised fine-tuning for alignment. RiC’s key features include simplicity and adaptivity, requiring only a single foundation model to be fine-tuned and supporting dynamic adjustments during inference time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make AI systems that are both helpful and harmless by figuring out how to align big models with what humans want. Right now, it’s hard to do this because we have to use reinforcement learning (RL) to fine-tune the models, which takes a lot of time and is unstable. The authors came up with a new way called Rewards-in-Context (RiC) that makes the process simpler and more flexible. RiC works by using multiple rewards within the prompt to guide the model’s response, and it only needs to be fine-tuned once. |
Keywords
* Artificial intelligence * Alignment * Fine tuning * Inference * Prompt * Reinforcement learning * Supervised
