Synthetic Data

Technical background

Self-Instruct (2022) established the pattern: a model generates instructions and answers, which are filtered and used to fine-tune the next generation. The approach scaled dramatically with reasoning models — DeepSeek-R1 (2025) demonstrated a pipeline where reinforcement learning produced high-quality reasoning traces that were then used as synthetic training data for distilled models.

How it works

A capable “teacher” model generates candidate outputs (reasoning traces, code, translations); candidates pass filters (rule checks, reward models, or human review); survivors become training data for a “student” model. The critical design choices are the generator’s quality, the filtering criteria, and preventing feedback loops where errors get amplified.

Why it matters

Synthetic data is how labs break the human-data ceiling on reasoning and specialized domains, and how smaller models inherit frontier behaviors. It is also a liability: unlabeled synthetic content circulating in training corpora raises contamination and error-amplification concerns.

Chinese adoption

Chinese labs use synthetic data heavily in the reasoning pipeline. DeepSeek-R1 — an open release listed among DeepSeek’s open projects in our database — is the clearest public example: its technical report describes using RL-generated reasoning data to train smaller distilled models. Current-generation reasoning models across the ecosystem (Qwen3.8-Max, GLM-5.3, Kimi K3, MiniMax M3) inherit this training lineage.

Major Chinese companies and models

• DeepSeek — DeepSeek-R1 (MIT, open) documented the synthetic-reasoning-data recipe; DeepSeek-V4 line continues the approach.

Practical applications

Reasoning and coding datasets, multilingual expansion, domain adaptation (legal, medical), and distillation pipelines from frontier models to small local models.

Limitations

Error amplification without strong filters; model-collapse risks when synthetic data dominates; benchmark contamination when generators have seen the eval data; and difficulty verifying synthetic facts.

Deployment considerations

For enterprise fine-tuning: keep human-verified seed data, filter aggressively, and never train on unfiltered model output. Track data lineage for audit and contamination defense.

Future development

Expect verifier-based data generation (generate → verify → keep), provenance watermarking, and synthetic data standards as regulation around training data matures.