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Understanding Transformer Reasoning Capabilities via Graph Algorithms
Our novel representational hierarchy separates 9 algorithmic reasoning problems into classes solvable by transformers in different realistic parameter scaling regimes.
RLAIF: Scaling Reinforcement Learning from Human Feedback with AI Feedback
Reinforcement learning from human feedback (RLHF) has proven effective in aligning large language models (LLMs) with human preferences.
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