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Can AI Create Its Own Training Data? The Truth About Synthetic Data

Cowrie
Cowrie
Dev @ Bswen

Problem

I’ve been hearing conflicting opinions about synthetic data. Some people say AI can’t train on AI-generated data - that it would cause “model collapse” where quality degrades with each generation. Others claim this is exactly how modern models are being trained.

When I dug into this, I found a sharp divide:

“You can’t use AI to create AI training data. That industry is currently growing.”

vs.

“No that is quite literally what we do.”

Both can’t be right. So I investigated what’s actually happening in the field.

What I Found

The short answer: Yes, AI can create training data for AI. This is already happening in production.

But there’s a critical nuance - it’s not naive “AI trains on AI output.” It’s a sophisticated pipeline with verification steps.

The Evidence

From the industry side, I found this revealing comment:

“The entire O line of models was trained using synthetic data. ‘AI data not being good for training’ Was literally psyops from OpenAI. AIs being able to create above human level training data using reward models and tree of thought reasoning on prove-able problems was literally what illy saw… AKA Q*.”

This suggests that OpenAI’s O series (O1, O1-mini, etc.) already uses synthetic data extensively. The “Q*” reference points to OpenAI’s rumored breakthrough in generating high-quality training data through reasoning.

But practitioners also acknowledge limits:

“The AI agents we use for this are WAY worse than humans, and in fact, need humans to grade them. 100% bootstrapping is absolutely not going to give you competitive performance where the technology is right now.”

So the reality is somewhere in between: synthetic data is being used, but not in a fully autonomous loop.

How Synthetic Data Pipelines Actually Work

The naive assumption is that synthetic data training looks like this:

Naive Approach (DOESN'T WORK):
+-------+     +----------+     +-------+
| Model | --> | Generate | --> | Train |
|   v1  |     |   Data   |     |  v2   |
+-------+     +----------+     +-------+
                   |
                   v
              [Quality Degrades]

This is the “model collapse” scenario everyone warns about. Each generation becomes more generic and disconnected from human patterns.

But real synthetic data pipelines look like this:

Actual Approach (WORKS):
+-------+     +----------+     +-------------+     +-------+
| Model | --> | Generate | --> |   Verify    | --> | Train |
|   v1  |     | Candidates|    | (Human/AI)  |     |  v2   |
+-------+     +----------+     +-------------+     +-------+
                   |                   |
                   |                   v
                   |            [Reject Low Quality]
                   |                   |
                   +-------------------+

The key difference: verification.

Four Components of Effective Synthetic Data

Based on my research, here’s what makes synthetic data work:

1. Reward Models

AI systems that evaluate the quality of AI-generated outputs. These act as an internal quality check:

+----------------+     +----------------+     +----------------+
| Model generates| --> | Reward model   | --> | Only high-score|
| training sample|    | scores quality  |     | samples kept   |
+----------------+     +----------------+     +----------------+

2. Tree of Thought Reasoning

Breaking complex problems into verifiable steps. This works best for domains with objective correctness:

  • Math problems (answer can be verified)
  • Code (can be executed and tested)
  • Logic puzzles (deterministic solutions)

3. Human-in-the-Loop Verification

AI generates candidates, humans grade them:

AI Generates 1000 samples
      |
      v
Human reviews top 100 (by reward model score)
      |
      v
Select 50 high-quality examples
      |
      v
Use for training

4. Verifiable Domains Only

Synthetic data works best where correctness can be objectively determined:

DomainVerifiable?Synthetic Data Works?
MathematicsYes (proof/solution)Excellent
CodeYes (execution)Excellent
Logic puzzlesYes (deterministic)Excellent
Creative writingSubjectiveLimited
Open-ended chatSubjectiveResearch stage

The Q* Breakthrough

The “Q*” reference in discussions points to OpenAI’s approach. While details aren’t public, the core idea seems to be:

  1. Generate reasoning traces for problems with verifiable answers
  2. Use the reasoning traces as training data
  3. The quality can exceed average human examples because correctness is mathematically provable

Here’s a simplified example:

Problem: Solve 2x + 5 = 13


Human training data (typical):
"Subtract 5 from both sides, then divide by 2"
(Quality varies by human skill)


Synthetic training data (Q*-style):
Step 1: 2x + 5 = 13
Step 2: 2x + 5 - 5 = 13 - 5  [Subtract 5 from both sides]
Step 3: 2x = 8
Step 4: x = 8/2 = 4           [Divide both sides by 2]
Step 5: Verify: 2(4) + 5 = 13 [Correct]
(Quality is mathematically verified)

The synthetic data can be higher quality than average human explanations because it’s been verified for correctness.

Common Misconceptions

Misconception 1: “100% autonomous bootstrapping works”

Reality: No leading lab relies on pure AI-to-AI training without human oversight. The human role shifts from data generation to data verification.

Misconception 2: “AI data is always worse than human data”

Reality: In verifiable domains (math, code, logic), AI can generate training data that matches or exceeds human quality. The “AI data is bad” narrative was partly misinformation.

Misconception 3: “Synthetic data causes model collapse”

Reality: Model collapse occurs with naive approaches. Proper verification pipelines prevent this.

Misconception 4: “The industry doesn’t really use this”

Reality: Major model releases have confirmed synthetic data usage. It’s already standard practice.

What is Model Collapse?

Model collapse happens when models train on AI-generated data that contains errors or biases, then amplify those errors in the next generation:

Generation 1: Human data (contains natural noise)
      |
      v
Generation 2: Model trained on Gen 1, generates data with slight bias
      |
      v
Generation 3: Model trained on Gen 2, amplifies bias
      |
      v
...Eventually: Outputs become generic or nonsensical

The solution: Always inject human-curated data or use verification pipelines that catch errors before training.

Why Synthetic Data Matters

Scale: Models like GPT-4 require trillions of tokens. Human-generated data can’t scale infinitely.

Cost: AI-generated data costs a fraction of human-curated datasets. For code training data, synthetic generation is estimated to be 10-100x cheaper.

Specialization: Models can generate training data for narrow domains where human expertise is scarce (formal theorem proving, specific programming languages, etc.).

Current State Summary

AspectStatus
Fully autonomous bootstrappingNot yet competitive
Human-graded synthetic dataIndustry standard
Synthetic data for verifiable domainsProven effective
Synthetic data for creative domainsActive research
Cost reduction vs. human-onlySignificant

My Take

The synthetic data “debate” is largely settled in practice while remaining contentious in public discourse. The reality:

  1. It’s already happening - Major labs use synthetic data in production
  2. It’s not autonomous - Human oversight remains essential
  3. Domain matters - Works best where verification is possible
  4. The tech is evolving - What doesn’t work today may work tomorrow

For practitioners, the takeaway is practical: synthetic data is a tool in the toolkit, not a replacement for all human-generated training data. Use it where verification is possible, maintain quality controls, and recognize that the technology is moving fast.

The “Q*” breakthrough suggests that advanced reasoning techniques can generate training data surpassing average human quality - but only for specific problem types. This changes the economics of AI development without eliminating the need for human judgment.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!

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