Summary of Search, Verify and Feedback: Towards Next Generation Post-training Paradigm Of Foundation Models Via Verifier Engineering, by Xinyan Guan et al.
Search, Verify and Feedback: Towards Next Generation Post-training Paradigm of Foundation Models via Verifier Engineering
by Xinyan Guan, Yanjiang Liu, Xinyu Lu, Boxi Cao, Ben He, Xianpei Han, Le Sun, Jie Lou, Bowen Yu, Yaojie Lu, Hongyu Lin
First submitted to arxiv on: 18 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); 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 The paper addresses the challenges posed by foundation models in machine learning, which require effective supervision signals to enhance their capabilities. The authors propose “verifier engineering,” a post-training paradigm designed for this era of powerful models. This approach involves automated verifiers performing verification tasks and providing feedback to foundation models. The process is divided into three stages: search, verify, and feedback. The paper provides a comprehensive review of the latest developments in each stage. Verifier engineering is seen as a crucial step toward achieving Artificial General Intelligence. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how machine learning models are getting more powerful but need help to get even better. The authors suggest a new way to improve these models called “verifier engineering.” It’s like having a team of robotic reviewers that check the model’s work and give it feedback. This process is divided into three steps: finding the right information, checking if it’s correct, and giving advice. The paper looks at what’s currently being done in this area and thinks it’s an important step towards making really smart AI. |
Keywords
» Artificial intelligence » Machine learning
