Are Large Language Models Taking Detours? Exploring the Interpretability of Transformer Reasoning Paths

This article interprets a study on the interpretability of internal representation paths in Transformers, focusing on whether there are redundant computations in the reasoning process of large language models and how to optimize reasoning efficiency. By probing the model's internal states and exploring early exit mechanisms, the study found compressible space between layers, task-dependent differences, and potential cost-saving opportunities, providing directions for reasoning optimization.

When large language models perform reasoning, Transformers process input tokens layer by layer. The core question is whether there is "detour" redundancy. The Transformer reasoning process includes converting tokens into vectors via the embedding layer, refining information through multiple Transformer blocks, and mapping to vocabulary probabilities via the output layer. The traditional view holds that each layer refines information, but its efficiency is questionable.

The study uses classic interpretability techniques to probe internal states, focusing on representation stability, convergence patterns, and redundant computations. It also explores the early exit mechanism—if a sufficiently good representation is formed in the middle layer, skip the remaining layers and output—to verify the existence of redundancy and the feasibility of optimization.

1. Compressible space exists between layers: In some tasks, the state changes moderately after the middle layer, so subsequent computations may not be necessary; 2. Task-dependent differences: Redundancy is more likely to occur in simple tasks than in complex reasoning; 3. Potential cost savings: Effective early exit can significantly reduce reasoning latency and costs.

Currently, large model reasoning costs are high (due to large parameter counts, full forward propagation, and sequential computation). If the number of layers can be reduced, costs can be directly lowered and efficiency improved. The long-term vision points to dynamic depth reasoning—adaptively determining the number of layers based on input complexity.

Limitations: Limited experimental scale, practicality to be verified, need to balance quality and efficiency; Future work: Verify findings on more models, develop reliable exit decision mechanisms, and combine with other optimization techniques.

1. Pay attention to reasoning efficiency—cost and accuracy are equally important; 2. Continuously follow community reasoning optimization solutions; 3. Balance the trade-off between efficiency and quality.