Reproduce “Seq-VCR: Preventing Collapse in Intermediate Transformer Representations for Enhanced Reasoning” on OpenResearch

About this paper

Md Rifat Arefin, Gopeshh Subbaraj, Nicolas Gontier, Yann LeCun, Irina Rish, Ravid Shwartz-Ziv, Christopher Pal

Sequential Variance-Covariance Regularization (Seq-VCR) prevents representation collapse in the intermediate layers of Transformer models. This method, combined with simple dummy pause tokens, enables models to solve complex arithmetic problems with near-perfect accuracy, often surpassing larger, state-of-the-art models that use Chain-of-Thought prompting.

Key insights

• Representation collapse in intermediate Transformer layers is a direct and measurable impediment to complex reasoning, especially in tasks requiring sequential operations like arithmetic carryovers.
• Enhancing internal representation diversity through regularization, even in simpler models, can unlock advanced reasoning capabilities previously thought to require explicit, human-supervised Chain-of-Thought prompting.
• The combination of Seq-VCR and pause tokens induces a 'phase transition' in learning dynamics, allowing models to rapidly acquire complex algorithms and generalize effectively to various reasoning tasks.

Summary by alphaXiv.