The Promise and Perils of Synthetic Data in AI

Synthetic data is a controversial topic in AI, here’s what it takes to bridge the gap between simulation and reality.

Post methodology: @claude-3.7 via Dust: write an essay for substack on the challenges and opportunities for using synthetic data in ai based on [jim fan’s ai ascent talk, a deep research report  (Challenges in Leveraging Synthetic Data) and relevant training data episodes]. be sure to read all of the attached documents and links before you write the essay. use the deep research report as the scaffolding for the argument and work the training data guest references into the text. Essay reworked in Gemini 2.5 Pro Canvas to add use case detail and condense length. Light editing, fact-checking and formatting for Substack.

The rapid evolution of AI has created an immense demand for high-quality, diverse training data. However, real-world data collection is often hampered by scarcity, high costs, privacy concerns, and the difficulty of capturing rare edge cases. Synthetic data—artificially generated information mimicking real data's statistical properties without using real-world information—offers a solution. Projections suggest that by 2030, synthetic data will form the majority of data used in AI training, marking a significant paradigm shift. This essay explores the dual nature of synthetic data: its transformative opportunities and formidable challenges, especially in fields like physical engineering and robotics where the simulation-to-reality gap is critical.

The Synthetic Data Revolution

The Data Scarcity Crisis: The scarcity of suitable training data is a major AI bottleneck. Jim Fan of Nvidia highlights this by contrasting research on LLMs, which draws on vast internet text, with robotics, where data like continuous joint control signals must be laboriously collected via methods such as teleoperation. Paul Eremenko of P-1 AI points to industrial engineering as an example of this scarcity. To train a large model you need millions of airplane designs as training data, but in the whole history of aviation since the Wright brothers there have only been only about a thousand distinct designs.

The Simulation Advantage: Simulation allows researchers to generate vastly more training examples than real-world collection permits. Fan describes real robot data collection as "burning human fuel." He says utilizing synthetic data lets Nvidia train its robotics model “10,000 times faster than real time.” Eric Steinberger's work with AI for poker showed that synthetic data from self-play and simulation enabled AI to explore strategies beyond human conception. Similarly, Max Jaderberg says he is a “big fan of synthetic data,” noting that Isomorphic Labs uses quantum mechanics principles to create scalable molecular dynamic simulations, forming a basis for extensive synthetic data.

Privacy Protection and Data Security: Synthetic data allows data use and analysis in regulated sectors like healthcare and finance without exposing personally identifiable information, ensuring compliance with regulations like GDPR or HIPAA. In enterprises, it enables AI system development with sensitive business data without risking confidentiality, fostering faster innovation with robust security.

Potential for Bias Reduction and Dataset Balancing: Synthetic data can create more balanced datasets by oversampling underrepresented groups or generating contrasting data to mitigate existing biases. For instance, techniques like SMOTE in tabular data help prevent models from being biased towards majority classes. Reinforcement learning systems benefit from diverse training scenarios generated synthetically, fostering more robust and fair AI. Crucially, bias evaluation of both seed data and the generation process is vital to ensure fairness.

Improved model performance: Synthetic data, particularly for smaller models, can significantly improve performance. Joe Spisak of Meta's Llama team notes its benefits in combination with distillation and efficient fine-tuning, often using larger models to generate examples for smaller ones. However, the reliability depends on the generator's output quality and careful filtering. Simpler methods like basic image augmentation also reliably boost robustness. Dean Leitersdorf of Decart demonstrated how generating synthetic data on their training cluster led to unexpectedly high hardware utilization, showcasing competitive advantages available through low-level engineering.

The Fundamental Challenges of Synthetic Data

Despite its advantages, synthetic data faces key challenges:

The Quest for Realism and Data Fidelity

Creating synthetic data that accurately mirrors real-world complexity is exceptionally difficult.

• Generating highly realistic and diverse image/video data from scratch often struggles with mode collapse, artifacts, and capturing fine-grained details.

• Complex, unstructured natural language text faces hurdles in maintaining coherence, factual accuracy, and nuanced meaning without bias or 'hallucinations'.

• Complex time-series data (e.g., financial markets) is hard to replicate due to non-stationary patterns and unforeseen events.

• High-dimensional tabular data suffers from the 'curse of dimensionality,' making it difficult to preserve subtle signals. Poor realism leads to AI models that perform badly in real-world applications. High-fidelity synthetic data must preserve original statistical properties and ensure privacy, capturing deep structural relationships. This fidelity to the real data distribution is crucial.

The Sim-to-Real Gap

A major hurdle is ensuring models trained on synthetic data generalize to real-world scenarios. The "sim-to-real gap" occurs when models excel in simulation but fail in reality, often because synthetic visual data doesn't fully replicate real-world noise, textures, and unpredictability. Domain randomization, as used by Jim at Nvidia (varying parameters like gravity and friction across thousands of simulations), aims to bridge this. While this has led to achievements like training robots to walk quickly in simulation, the gap remains an ongoing challenge requiring refined simulation and real-world validation. The ultimate test is enhanced generalization power on unseen real data.

Bias Amplification and Reinforcement

Synthetic data generation can inherit and worsen biases from original seed data or generation algorithms. Flawed source data leads to flawed synthetic data. Careful monitoring and validation processes are essential, as synthetic data isn't inherently unbiased.

Not All Data is Equally Simulatable

The scarcest data (e.g., in robotics) is often the hardest to generate reliably for real-world use.

Some types of data are more reliably generated for ML:

• Simple tabular data for augmentation/balancing: Techniques like SMOTE work well if the underlying structure isn't overly complex. Task appropriateness is key.

• Image data augmentation (geometric/color transformations on real images): Standard practices like rotations improve robustness without altering semantic content.

• Structured/templated text data: Useful for NLU tasks with constrained linguistic variation (e.g., intent recognition).

• Data from highly accurate simulators (with caveats): Valuable for capturing rare scenarios if fidelity is high, but the 'sim-to-real' gap needs robust validation.

Conversely, reliable generation is harder for:

• Highly realistic image/video data from scratch: Issues like mode collapse hinder generalization.

• Complex, unstructured natural language text: Risks of inaccuracies and bias limit reliability for tasks needing deep understanding.

• Complex time-series data: Replicating intricate dynamics is a major hurdle.

• High-dimensional tabular data: The 'curse of dimensionality' makes preserving subtle signals challenging. Data from deterministically provable systems (math, physics) can be reliably generated if rules are known. Challenges arise with unknown, probabilistic, or emergent complexity common in open-ended text or real-world imagery.

Model Collapse and Information Degradation

Repeatedly training AI models on synthetic data from other AI models in a closed loop can cause "model collapse"—a progressive loss of quality, diversity, and precision, especially at data distribution extremes. Ioannis Antonoglou of Reflection.ai notes, “new methods never work out of the box.” Without fresh real-world input or novel generation strategies, AI systems risk creating simplified, distorted realities. For RL agents, Ioannis emphasizes, “It’s important that these systems start taking actions, they start learning from their own mistakes.”

Domain-Specific Challenges: Physical Engineering and Robotics

These fields face amplified challenges:

Adherence to Physical Laws and Multi-Physics Interactions: In physical engineering, AI outputs must conform to physics laws to avoid failures or safety risks, as Paul from P-1 emphasizes. Standard generative models often lack this understanding, potentially "hallucinating" impossible designs. He states synthetic data here must be “physics-based and supply chain-informed,” a considerable challenge when design spaces involve millions of parts.

Computational Costs and Scaling Challenges: Physics-based simulations (FEA, CFD) are computationally expensive. This creates a tension between the need for accuracy (rigorous simulations) and large datasets (faster, potentially less accurate methods). Making high-fidelity simulation more efficient is key. Harmonic, for instance, generates vast synthetic mathematical data to train models for proving advanced theorems, tackling a "data-poor regime."

The Future of Synthetic Data

Key trends include:

Blending real world and synthetic data: Hybrid approaches combining synthetic and real-world data are promising, as relying solely on synthetic data can limit accuracy. Paul envisions P-1's AI engineer learning initially from non-proprietary synthetic data, then from customer data behind firewalls.

AI-Accelerated Simulations and Multi-Modal Data Generation: Using ML to speed up traditional physics solvers or learn approximations is a promising direction, as Jim describes in Nvidia's work on simulation environments. Multi-modal synthetic data generation (geometric, textual, numerical, visual) is also key for enhancing realism in inherently multi-modal engineering problems.

The Physical API Vision: Jim envisions Nvidia creating a "physical API," allowing AI to interact with the physical world as easily as software APIs do with digital systems, revolutionizing fields like manufacturing and healthcare.

Reimagining Synthetic Data for Robot Learning: Nvidia's DreamGen paper presents a new paradigm, using video world models as data generators for "neural trajectories." This synthetic robot data, with automatically extracted pseudo-actions, enables remarkable generalization from minimal real-world data (10-13 trajectories). This approach achieves significant success rates on novel behaviors in unseen environments, offering a new path for scaling robot learning.

Navigating the Synthetic Data Frontier

Synthetic data holds immense promise for AI, especially where real-world data is limited. Realizing this potential requires overcoming significant challenges, particularly in physical systems where model inaccuracies have severe consequences. As AI increasingly engages with the physical world, the ability to generate high-quality, physically accurate synthetic data will be critical for progress, positioning those who master it to lead future AI innovation.