Data Processing: The 'Janitorial' Work That Builds AGI
We delete 99% of the internet before it touches our models. Why 'Garbage In, Garbage Out' is the only law that matters in AI training.
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A model is only as good as the data it consumes. Garbage in, garbage out. At Reinforced, we treat data curation not as a janitorial task, but as a high-stakes engineering discipline. We process petabytes of raw information to distill the 'knowledge' required to train our foundation models.
The Data Pipeline: Deleting the Noise
The public web is a dumpster fire. It contains everything from Shakespeare to SEO spam, from Python kernels to cat memes. Our pipeline's job is to separate the signal from the noise. We utilize a distributed architecture built on Ray and Spark to parallelize these operations across thousands of nodes, allowing us to ingest and process data at line rate.
Quality Filtering: Textbooks Are All You Need
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We use a multi-stage filtering process. First, heuristic filters remove obvious junk. Then, we use lightweight 'classifier models' to score the educational value. We prioritize textbooks, encyclopedias, and high-quality discourse. If it reads like a clickbait article, it's gone.
De-duplication: Memory is not Intelligence
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The web is incredibly repetitive. Training on duplicates causes the model to 'overfit' (memorize) specific phrases rather than learning concepts. We employ MinHash LSH (Locality Sensitive Hashing) to identify and remove near-duplicates. We don't want the model to memorize the answer; we want it to derive it.
Synthetic Data: The Infinite Teacher
Human-generated data has its limits. To bridge the gap, particularly in complex reasoning chains and math, we employ synthetic data generation. We use our strongest models to generate high-quality, step-by-step solutions to complex problems, which are then verified by formal verifiers. This is how we bootstrap intelligence.
