The Messy Reality of Scale: Synthetic Data and Pre-Training — Marah Abdin & Robert McHardy, poolside

2026-07-25 · Source: AI Engineer · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

Poolside has developed a sophisticated synthetic data and pre-training methodology to overcome the scarcity of good code data, enabling the creation of a 118 billion parameter model for agentic coding. Their synthetic pipeline combines templates with supplementary context, varying phrasing and tuning difficulty to ensure effective learning. Multistage pipelines transform existing data, allowing for character style or plot changes and converting single prompts into multi-turn chats, with an orchestrator policing and discarding subpar generations. During pre-training, poolside employs a "trust nothing" approach, requiring identical numerical outputs from two model replicas on the same data; any mismatch terminates the run. This rigorous process uncovers critical failures like spiky loss from broken GPUs, numerical precision bugs, and silently corrupted gradients from race conditions. This recipe has scaled successfully, with early results already surpassing GLM 4.5 Air.

Key takeaway

For Machine Learning Engineers building large-scale models with synthetic data, you should prioritize data quality and training integrity. Implement robust synthetic data pipelines that tune difficulty and use orchestrators for quality control. Crucially, adopt a "trust nothing" pre-training philosophy, like running duplicate model replicas and terminating runs on numerical mismatches, to surface subtle but critical failures early.

Key insights

Effective synthetic data generation requires careful difficulty tuning and rigorous quality control throughout the training process.

Principles

• Synthetic data must be teachable, not just plentiful.
• Training integrity demands zero-tolerance for numerical mismatches.
• Proactive failure detection is crucial for large-scale model development.

Method

Poolside's synthetic pipeline uses templates, context, varied phrasing, and tuned difficulty. Multistage pipelines port data, with an orchestrator policing generations. Pre-training mandates two model replicas return identical numbers on same data, terminating on mismatch.

In practice

• Implement difficulty tuning for synthetic data tasks.
• Use multi-stage pipelines to diversify existing datasets.
• Employ replica-based validation to detect training inconsistencies.

Topics

• Synthetic Data
• Pre-training
• Large Language Models
• Agentic Coding
• Model Training Integrity
• GLM 4.5 Air

Best for: Research Scientist, AI Engineer, AI Scientist, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI Engineer.