Five Checkpoints to Bitwise-Deterministic Training Runs
A five-checkpoint workflow for making large-scale training runs bitwise reproducible: pick the guarantee, fingerprint the run, and trace divergence down to the kernel.
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1 post about Determinism. Every summary links to the original.
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A five-checkpoint workflow for making large-scale training runs bitwise reproducible: pick the guarantee, fingerprint the run, and trace divergence down to the kernel.
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The post introduces Level 1 of a six-level maturity model for production LLM systems, the determinism layer, which contains the model to a single node so the surrounding system is ordinary, testable code. The core design move is making the agent a pure function that proposes decisions without side effects, with a separate substrate that applies proposals only after approval. The system uses a fixed graph of nodes where the LLM is used only in one node for judgment, and all other nodes are deterministic, testable code. Structured output is enforced via schema constraints and validation, and confidence is composed from independent signals rather than taken from the model. For capabilities requiring autonomy, a bounded loop with a step cap and tool allow-list is used, but the default is the fixed graph to maintain predictability and auditability.
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