The Messy Reality of Scale: Synthetic Data and Pre-Training — Marah Abdin & Robert McHardy, poolside
The presentation details how poolside scaled their LLM training from smaller models (Laguna M/XS) to a massive 118 billion parameter model for agentic coding. The core strategy involves creating sophisticated synthetic data pipelines—which complement organic data by exposing implicit rationale and structure—and implementing extreme rigor in the pre-training process. Key technical challenges addressed include catching silent failures like broken GPUs, numerical precision loss during tensor parallel accumulation (BF16 to FP32 fixes), and race conditions via weight hashing checks. The resulting model, Laguna S, demonstrates competitive performance against industry leaders on coding benchmarks.
Key takeaways
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Synthetic Data for Feature Extraction
10:37
Synthetic data is used not to replace organic data but to complement it by extracting and projecting implicitly hidden features (e.g., implicit rationale, planning, structure). Pipelines are modular, allowing complex tasks to be broken down into stages like multi-stage workflows, cross-domain porting (e.g., math problems to code), or multi-turn chats.
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Rigorous Training Validation: Trust Nothing
15:03
To ensure correctness at scale, the team implemented model replica hashing checks; if hashes over weights do not match across distributed replicas, training is immediately halted. Failures observed include silent data corruption from broken GPUs and gradient corruption due to race conditions.
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Scaling Success: The 118B Parameter Model
The final model, Laguna S (118B parameters), was trained on 30 trillion tokens across 4,000 GPUs. Early results show strong performance in agentic coding benchmarks (e.g., SWEETbench), outperforming models like GLM 4.5 Air.
