MODULE 2: DATA ENGINEERING & SYNTHETIC DATA · SCENE 05
Embedding Clustering & Deduplication
MinHash dedup and embedding-space clustering that keeps topic coverage balanced across the post-training corpus.
19,600 PROMPTS
One cluster at 45% of the corpus is a diversity bug — downsample it, feed the thin leaves.
ARCHITECTURE & FORMULATIONModule 2: Data Engineering & Synthetic Data
Embedding diversity and MinHash deduplication
Embedding-space clustering turns an unstructured pile of examples into a measurable distribution. MinHash-LSH removes near-duplicates cheaply; cluster counts expose which capability regions are over- and under-represented.
MinHash + LSH
Shingle each document, hash it into a signature, bucket via locality-sensitive hashing. Near-duplicates collide without an O(n²) all-pairs comparison.
Cluster Coverage
Embed prompts, cluster with k-means or HDBSCAN, then inspect cluster sizes. A single cluster holding 40% of the data is a diversity bug.
Deliberate Rebalancing
Downsample bloated clusters, synthesize examples for empty ones. Coverage is a design decision.
MATHEMATICAL FORMULATION · SIGNATURE SIMILARITY
J(A, B) ≈ agree(MinHash_sig_A, MinHash_sig_B) / signature_length
The fraction of matching min-hashes estimates Jaccard similarity between document shingle sets; LSH turns that estimate into cheap candidate pairs.
REAL-WORLD PRODUCTION ENGINEERING
The FineWeb and Dolma pipelines dedup trillions of tokens with MinHash-LSH before any model sees the data.
Teams keep a 'cluster dashboard' during SFT data review — removing one over-represented cluster often fixes a specific behavioral tic.