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synthetic-data

synthetic training data generation

self-instruct

SystemTechniques · 8Primitives · 43 synthetic-data LLM-as-judgemachine-learning · medium: Grade open-ended outputs with a language model under a rubric, then correct its known biases by order swapping, voting and calibration against human labels.›Near-duplicate detection with MinHa…machine-learning · medium: Estimate set Jaccard similarity from k min-hashes and find similar pairs with banded LSH buckets; the standard dedup for web crawls, training corpora and sequence collections.›Knowledge distillationml-training · medium: Train a small student to match a large teacher's softened outputs (plus the hard labels) so it keeps most of the teacher's accuracy at a fraction of the cost.›Verifiable rewardsml-training · medium: Rewards from programmatic checkers (unit tests, math answer matching) for RL and data filteringEgress proxy filteringsecurity · medium: Forcing sandbox network traffic through a proxy with domain allowlists, TLS SNI checks and request loggingMicrovm isolationsecurity · medium: Minimal KVM-based VMs (Firecracker-style) with tiny device model, fast boot and snapshot restoreProcess sandboxingsecurity · medium: Run risky work in a separate, deliberately powerless process and let a small privileged broker do the few things it needs, over a narrow pipe.›Token samplingml-training · low: Turn a vector of scores into one chosen item with temperature, top-k, top-p, min-p, repetition penalties and a seed.›Agent sandboxingagentic-ai · unknown: Constrained decodingagentic-ai · low: Force LLM output to match a regex, JSON schema or grammar by masking, at every step, the tokens that would leave the language.›Prompt cachingagentic-ai · low: Reuse the attention keys/values of a repeated prompt prefix, so an agent's growing context is prefilled once and later turns pay only for the new tokens.›Embeddingsmachine-learning · low: A learned table mapping discrete ids to dense vectors: gather forward, sparse scatter-add backward, row-wise optimizers to keep huge tables cheap.›k-means clusteringmachine-learning · low: Partition points into k clusters by alternating nearest-centroid assignment and centroid averaging; k-means++ seeding makes it dependable.›Continuous batchingml-training · low: Schedule LLM inference per iteration, not per request; finished sequences leave and new ones join the running batch every step, which keeps the GPU full.›Rejection samplingml-training · low: Best-of-n and filtered sampling: generate many candidates, keep those passing a verifier or reward thresholdAnnotator agreementstatistics · low: Inter-annotator agreement measures: Cohen and Fleiss kappa, Krippendorff alpha, gold-question accuracyBradley terry modelstatistics · low: Pairwise comparison model P(i beats j)=s_i/(s_i+s_j); MLE fit, link to Elo and reward modelsFast hash functionsdata-structures · low: Non-cryptographic hashes built from 64×64→128-bit multiplies and xor-shifts; pick by key size, trust level and whether the hash is persisted.›Rolling hashdata-structures · low: Rabin-Karp polynomial rolling hashes and winnowing for substring fingerprints and document matchingSwiss tabledata-structures · low: Open-addressing hash table that filters 8-16 slots at once with 1-byte hash tags and SIMD, giving fast lookups at 87.5 % load.›Data augmentationmachine-learning · low: Generate label-preserving variants of each training example on the fly to enlarge the effective dataset and bake in invariances.›Loss functionsmachine-learning · low: The scalar a model minimizes: cross-entropy for classes and tokens, MSE or Huber for values, each with a stable form and simple gradient.›Pass at kmachine-learning · low: Unbiased pass@k estimator from n samples with c correct, for code and agent benchmarks+ 1 more techniques+ 27 more primitives

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