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A new research initiative by Sakana AI Labs, an AI research company based in Tokyo, Japan, and founded by former Google researchers, is using large language models (LLMs) to automate AI research and discovery. This innovative approach aims to improve the training of foundation models by leveraging LLMs to generate hypotheses and code. The process has led to the development of DiscoPOP, a novel algorithm that optimizes large language model outputs by merging logistic and exponential methods, surpassing traditional techniques. This advancement is seen as a significant step towards achieving Artificial General Intelligence (AGI), defined as an AI capable of performing human-level tasks.
How well do DPO and PPO work on public preference datasets? Excited to share some work exploring the effects of data, reward models, and prompts! We also find that PPO generally beats DPO, despite being more challenging engineering-wise. 📜: https://t.co/niXEHuPK1S More below 👇 https://t.co/oxuta479tm
Transform the Future with AI Agents! 🙌 Discover how AI Agents harness the power of LLMs to revolutionize complex tasks through reflection, planning, collaboration, and tool-use. https://t.co/mTghu2e8C3
Discovering Preference Optimization Algorithms with and for Large Language Models ◼ 🚀 New research transforms Large Language Model outputs! DiscoPOP, a novel algorithm derived from LLM-driven objective discovery, eclipses traditional methods by merging logistic & exponential… https://t.co/RXXl5kUm0k
