Search Results for author:
Found 2 papers, 0 papers with code
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 vs. RLHF: 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, but gathering high-quality preference labels is expensive.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
