The Use of Synthetic Data in AI Training

I recently saw a video by Matthew Berman on the advances in the use of synthetic data in AI training. I found myself a bit confused about how this worked, so I had a conversation with ChatGPT about it.

Here’s the article the video was based on: https://arxiv.org/abs/2505.03335

The article is quite long and quite challenging to read if you don’t have a background in computer science, which I do not. So I asked ChatGPT to summarize it and then had some follow-up questions. I also wrote a companion article on my Chinese Herb Academy blog specifically applying these ideas to Chinese medical education.

The summary begins here:

Large language models (LLMs) have recently achieved significant gains in reasoning by using reinforcement learning with verifiable rewards (RLVR). However, even “zero” RLVR methods—which avoid reasoning-trace supervision—still rely on human‐curated question–answer pairs for training, raising concerns about scalability as AI capabilities outpace the availability of high‐quality human data .

Absolute Zero Paradigm
To eliminate any dependence on external data, the authors introduce the Absolute Zero paradigm, in which a single model simultaneously proposes new reasoning tasks optimized for learnability and solves them, using verifiable feedback from its environment as the only reward signal . Concretely, the model plays two roles—π_propose to generate task specifications and π_solve to answer them—and is trained end‐to‐end via a composite RL objective that balances a “learnability” reward (encouraging tasks of moderate difficulty) with the standard solution correctness reward .

Absolute Zero Reasoner (AZR)
As the first instantiation of this paradigm, the Absolute Zero Reasoner (AZR) uses a Python code executor as its environment, grounding tasks in code induction, abduction, and deduction. At each iteration, AZR:

1. Proposes code‐based triplets (program, input, output) across the three reasoning modes, filtering for syntactic correctness, safety, and determinism.
2. Solves the resulting tasks, receiving binary correctness feedback.
3. Updates its unified policy with a multitask REINFORCE++ algorithm tailored to separate baselines for each (task, role) configuration .

Experimental Results
AZR is evaluated on out‐of‐distribution benchmarks in both code (HumanEval+, MBPP+, LiveCodeBench) and math (AIME’24, AIME’25, AMC’23, Math500, Minerva, OlympiadBench). Despite never seeing any human‐curated examples:

• AZR‐Coder‐7B achieves a combined code+math average of 50.4%, surpassing the previous best zero‐setting model by 1.8 points and models trained on tens of thousands of examples by 0.3 points in the coding category .
• Cross‐domain gains are striking: math accuracy rises by 15.2 points for AZR‐Coder‐7B versus just 0.65 points for expert‐trained code models, demonstrating strong general reasoning improvements.
• Scaling benefits: larger models (3B, 7B, 14B) yield progressively bigger gains (+5.7, +10.2, +13.2 overall), and the paradigm even transfers to weaker model classes like Llama3.1‐8B with moderate improvements .

Conclusion and Future Directions
The work establishes that general reasoning skills can emerge without any external supervision, through self‐play and environment‐grounded feedback alone. AZR not only matches but surpasses existing zero‐data baselines, scales efficiently, and works across model classes. The authors have open‐sourced their code, models, and logs to spur further research. Future avenues include expanding the environment beyond code (e.g., web‐based verification, formal mathematics, embodied simulators) and exploring richer agentic or scientific task domains under the Absolute Zero paradigm .

Sources

o4-mini

You said:

I’d like a concrete example of how this would work with something like an AI medicine agent.

ChatGPT said:

Here’s a step-by-step illustration of how the Absolute Zero paradigm could be applied to train an AI Medicine Agent—let’s call it AZ-Med—using only self-generated tasks and environment-grounded feedback:

---

1. Define the Two Roles

1. πₚᵣₒₚₒₛₑ (Task Proposer)
   • Invents new clinical‐style problems of moderate difficulty, e.g. “Given these symptoms and lab values, what is the most likely diagnosis?”
2. πₛₒₗᵥₑ (Task Solver)
   • Attempts to answer the proposed problems.

Both roles share the same underlying model parameters and are trained jointly.

---

2. Choose a “Medicine Simulator” Environment

We need an environment that can verify solutions without human labels. For medicine, this might be a clinical case simulator that:

• Takes as input a patient profile (age, sex, history, vitals, labs).
• Contains “ground-truth” logic or a hidden diagnostic engine.
• Returns a binary reward:
  • 1 if the solver’s diagnosis/treatment matches the simulator’s,
  • 0 otherwise.

---

3. Task Generation Example

1. Proposal
   • πₚᵣₒₚₒₛₑ samples a patient scenario, for instance:
     {
   • “age”: 65,
   • “sex”: “F”,
   • “symptoms”: [“chest pain”, “shortness of breath”, “diaphoresis”],
   • “vitals”: {“BP”: “150/90”, “HR”: 110, “SpO₂”: “94%”},
   • “labs”: {“troponin”: “0.8 ng/mL”, “CK-MB”: “7 ng/mL”}
   • }
   • It pairs this with a prompt:Prompt: “Based on the above, what’s the most likely diagnosis?”
2. Filtering
   • The proposal is checked to ensure it’s medically plausible (e.g. troponin in a realistic range) and not trivially easy or impossibly hard.

---

4. Task Solving and Feedback

1. Solver
   • πₛₒₗᵥₑ reads the case and answers, e.g.:“Acute myocardial infarction (NSTEMI).”
2. Verification
   • The simulator compares this answer to its hidden “correct” label (it was seeded as NSTEMI) and returns reward = 1.
   • If the solver had answered “unstable angina,” reward = 0.

---

5. Reinforcement Learning Loop

At each iteration:

1. Collect Tasks & Solutions
   • Batch of N proposed cases + solver’s answers.
2. Compute Rewards
   • For each case:
     • R_correctness ∈ {0,1} from the simulator.
     • R_learnability = a function that rewards tasks whose solution probability is neither too high nor too low (encourages moderate difficulty).
3. Update Model
   • Apply a combined RL objective:L=−E[Rcorrectness+λ Rlearnability]  log⁡πrole(action∣state)L=−E[Rcorrectness​+λRlearnability​]logπrole​(action∣state)
   • Roles share parameters but use separate baselines for stability.

---

6. Sample Iteration Walk-Through

Step	πₚᵣₒₚₒₛₑ Output	πₛₒₗᵥₑ Output	Reward
1. Propose	Patient: 45 M, fatigue, weight gain, cold intolerance; labs: TSH = 8 mIU/L	—	—
2. Solve	“Primary hypothyroidism.”	—	—
3. Verify	—	—	R_correctness = 1
4. Learnability	—	—	R_learnability ≈ 0.4
5. Update	Both proposer & solver get gradients toward generating/solving similar tasks of this difficulty level.	—	—

Over thousands of such cycles, AZ-Med learns both how to propose clinically meaningful, solvable medical cases andhow to diagnose them—without ever ingesting a single real patient record or human‐crafted QA pair.

---

7. Scaling Up

• Beyond Diagnosis: Propose and solve treatment planning or prognostic tasks.
• Environment Enrichment: Use more sophisticated clinical simulators (e.g. virtual patient models that simulate physiology).
• Multi-Modal Input: Extend cases to include images (X-rays, ECGs) and have the agent generate/interpret them, with automated image‐based rewards (e.g. matching a reference segmentation).

---

Why This Works

• No Human Labels Needed: The simulator provides the only feedback.
• Self-Paced Difficulty: Learnability rewards keep the agent challenged.
• Generalization: As the agent invents novel but structured cases, it acquires broad medical reasoning skills that transfer to real-world benchmarks once fine-tuned.

This Absolute Zero approach could thus bootstrap an AI medical agent’s core clinical reasoning entirely “from scratch,” ready for subsequent real-world data fine-tuning.

You said:

So, it is just the reasoning process that can be done without human input or supervision. However, these models still have a foundation of training in medical information available on the open Internet and/or proprietary databases. Correct?

ChatGPT said:

Thought for 9 seconds

Exactly—Absolute Zero only eliminates the need for additional human-curated data during the reinforcement-learningphase, but it still relies on a pretrained foundation model that has already ingested massive amounts of text (e.g. web-scraped data, scientific articles, or proprietary corpora).

In fact, the very first step of AZR is to “Require: Pretrained base LLM π₀” (the model you’ll self-play on) . In the paper’s experiments, AZR-7B uses Qwen2.5-7B-Base—a model already trained on large-scale data—as its starting point .

So, if you built an “Absolute Zero Medicine Agent,” you would similarly begin with an LLM that’s been pretrained on medical literature (open-access journals, clinical guidelines, electronic health record text, etc.). The self-play loop then bootstraps the agent’s clinical reasoning purely via task proposal, solving, and environment-verified feedback—but it never replaces the underlying medical knowledge the model picked up during its initial pretraining.

Sources

You said:

So in the case of the medical example, the model would be generating a large number of synthetic cases to develop and improve its ability to reason ?

ChatGPT said:

Exactly—that’s the core idea. In our medical-agent example, the model wouldn’t just see a handful of curated cases; it would loop through thousands or even millions of self-generated clinical scenarios, each of which serves as a mini “puzzle” to sharpen its diagnostic reasoning. Here’s how that looks in practice:

1. Massive Task Generation
   • Volume: At each training iteration, πₚᵣₒₚₒₛₑ spins up hundreds or thousands of new patient profiles, sampling age, sex, symptoms, vital signs, lab results, etc., from learned distributions.
   • Curriculum via Learnability Reward: Tasks that are too trivial (e.g., “TSH = 100 mIU/L ➔ hypothyroidism?”) or impossibly hard (nonsensical lab combinations) get down-weighted or filtered out. The model learns to hit a “sweet-spot” of moderate difficulty.
2. Self-Play Loop at Scale
   • Solve & Verify: For every synthetic case, πₛₒₗᵥₑ proposes a diagnosis or treatment. The clinical simulator returns a binary reward (correct/incorrect).
   • RL Update: Both proposer and solver update their shared parameters, nudging πₚᵣₒₚₒₛₑ toward producing more insightful, solvable cases and πₛₒₗᵥₑ toward better reasoning strategies.
3. Iterative Refinement
   • Over millions of such proposal–solve–verify cycles, the agent builds a rich internal model of medical reasoning: pattern recognition, differential diagnosis, lab interpretation, etc., all without ever touching a real patient record.
4. Transfer to Real-World Tasks
   • Once the core reasoning is honed, you can fine-tune on smaller sets of real clinical cases or benchmark datasets—now requiring far less human-labeled data because the heavy lifting of “learning to think like a clinician” has already been done.

So yes—the brilliance of Absolute Zero in medicine is that it bootstraps reasoning by generating a vast, evolving curriculum of synthetic cases, letting the agent teach and test itself at scale.

You said:

How does the model assess whether the reasoning it did in a synthetic case is accurate if no expert humans are involved in the verification process?

ChatGPT said:

Thought for 7 seconds

The key is that you replace human “right” or “wrong” judgments with a built-in oracle—the simulator or executor that knows the ground truth for every synthetic case. In other words, no person needs to look at each example, because:

1. Task specification embeds the answer.
   When πₚᵣₒₚₒₛₑ generates a task, it also (implicitly or explicitly) fixes its “correct” outcome in the simulator’s hidden state.
   • In the code domain (AZR paper), the simulator is a Python executor: you generate a program + input/output pair, and “correctness” is simply whether the solver’s code reproduces the exact output when run .
   • In our medical example, the “clinical case simulator” contains a hidden diagnostic engine: each synthetic patient profile is seeded with the true diagnosis or treatment outcome.
2. Automated verification.
   The simulator runs the solver’s answer through its own logic and returns a binary reward:
   • 1 if the solver’s output matches the hidden label,
   • 0 otherwise.
     This check is deterministic, replicable, and requires no human oversight.
3. Learnability filter.
   To avoid trivial or impossible problems, a secondary “learnability” reward down-weights cases that are either too easy (the solver always gets them right) or too hard (the solver always gets them wrong). This ensures the curriculum stays in the sweet spot of challenge.
4. No human labels in the RL loop.
   Once you’ve built your simulator (code executor, clinical rules engine, etc.), the entire propose-solve-verify cycle runs without further human intervention. All supervision is “self-generated” via the oracle you’ve encoded.

So the model’s “proof” of correct reasoning comes from passing the test set by the simulator itself—just like a student knows they got a math problem right because the answer key matches their solution, except here both problems and keys are created, managed, and checked by the AI-driven environment.