Grokking Transformers: Achieving Near-Perfect Causal Reasoning

In this video, the host from code_your_own_AI delves into the concept of ‘grokking’ transformers and their ability to achieve near-perfect causal reasoning. The video explains how grokking works by enabling transformers to identify hierarchical structures within human sentences, leading to the formation of ‘generalizing circuits.’ These circuits are crucial for efficiently encoding and retrieving knowledge for reasoning tasks. The discussion covers the essential elements needed to create grokked transformers, such as extensive training, optimal transformer architecture depth, and a well-designed training dataset incorporating atomic and inferred facts. The video also highlights the use of out-of-distribution examples to test the generalization capabilities of grokked transformers.

The host introduces two tasks where grokked transformers excel: composition and comparison. The video explains the importance of the ratio of inferred to atomic data, the number of transformer layers, and the data distribution within the training set in influencing grokking performance. Techniques like logic lens and causal tracing are used to understand how grokking transformers work by analyzing internal activations and mapping causal pathways through the transformer’s layers. The video concludes by emphasizing the potential of grokking transformers in achieving near-perfect causal reasoning and optimizing transformer architecture for better reasoning performance.

The video also previews future content, including a deeper analysis of why grokking works better for comparison tasks and a comparison of fully grokked GPT systems with state-of-the-art RAG systems, such as GPT-4 Turbo and Gemini 1.5 Pro, to determine which performs better in reasoning tasks.