Most LLMs, like ChatGPT, are aligned using reinforcement learning from human feedback (RLHF). Superhuman models may exhibit behavior beyond human comprehension, making alignment challenging. OpenAI researchers proposed weaker models supervising stronger ones, achieving promising results in NLP and chess tasks. Their open-source code and grant programs aim to advance this research.
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Unlocking Superhuman AI’s Full Capabilities
Aligning Superhuman AI Models with Weak Supervisors
Many AI models, like ChatGPT, are trained using reinforcement learning from human feedback (RLHF). However, when dealing with superhuman models that perform complex behaviors beyond human comprehension, aligning these models becomes a challenge. Researchers at OpenAI have proposed a solution by using weaker models to supervise stronger ones.
Research Findings
The researchers experimented with weak-to-strong generalization in various settings, such as NLP tasks, chess puzzles, and reward modeling. They found that using weak supervisors, they were able to recover much of the capabilities of superhuman models, such as GPT-4. They also observed that auxiliary loss and bootstrapping with intermediate model sizes improved weak-to-strong generalization in certain tasks.
Practical Implications
While the research has some limitations, it serves as a promising starting point to address the challenge of super alignment in AI. The researchers have made their code open-source and launched grant programs to encourage further research in this area.
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