Summary of Learn Your Reference Model For Real Good Alignment, by Alexey Gorbatovski et al.
Learn Your Reference Model for Real Good Alignment
by Alexey Gorbatovski, Boris Shaposhnikov, Alexey Malakhov, Nikita Surnachev, Yaroslav Aksenov, Ian Maksimov, Nikita Balagansky, Daniil Gavrilov
First submitted to arxiv on: 15 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 proposes a new paradigm for Large Language Models (LLMs) alignment called Trust Region, which dynamically updates the reference policy throughout the training process. The authors aim to mitigate overoptimization, where trained models deviate excessively from the reference policy, leading to decreased sample quality. They introduce variants TR-DPO, TR-IPO, and TR-KTO, demonstrating their effectiveness in reducing overoptimization through toy examples and specific tasks such as helpful dialogue, summarization, and general-purpose assistant setups with the Llama3 model on AlpacaEval 2 and Arena-Hard benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps fix a problem with Large Language Models (LLMs) that can make them less accurate. When training these models, they sometimes stray too far from what we want them to do, making their results worse. The authors introduce a new way to align LLMs, called Trust Region, which keeps the model on track by updating its goals during training. They show that this approach works well in various tasks, such as creating helpful conversations and summarizing text. |
Keywords
» Artificial intelligence » Alignment » Summarization
