Training-Inference Kernel Contracts: Bounding Divergence in Post-Training and Deployment

Researchers propose 'kernel contracts,' a framework for managing divergence between training and inference implementations of AI models that operate at different precision levels. The work formalizes how finite-precision optimizations can produce different outputs at identical weights and provides mathematical bounds on resulting policy drift, with implications for reliable AI deployment.

Modern AI systems face a fundamental engineering challenge: the code optimizing models during training differs substantially from the code serving them in production. Training kernels prioritize automatic differentiation, while inference kernels optimize for speed through low-precision arithmetic and dynamic batching. In finite precision environments, these architectural differences can produce measurably different outputs despite identical model weights, creating a source of systematic error that current practices largely ignore.

This research emerges from the intersection of ML systems engineering and formal verification. As models scale and deployment becomes more performance-critical, the gap between training and inference behavior has grown from theoretical curiosity to practical problem. The paper contextualizes this within post-training optimization of reinforcement learning policies, where small divergences between evaluation and deployment can compound into significant performance degradation.

The practical impact centers on reliability and reproducibility. For organizations deploying large language models or RL systems, unaccounted kernel divergence introduces hidden variance in benchmark performance. The framework proposes contractual specifications—combining numerical precision bounds, statistical guarantees, runtime constraints, and observability metrics—alongside automated enforcement through routing decisions. This enables teams to quantify acceptable divergence explicitly rather than discovering problems in production.

The work's limitation is frank: no production-scale empirical validation is reported. The framework remains theoretical and architectural rather than demonstrated at scale. However, the formalization provides vocabulary for engineering teams to discuss kernel contracts systematically. The four-stage promotion pipeline and YAML DSL suggest implementable tools. Future work will determine whether this framework becomes standard practice for responsible AI deployment or remains specialized to extreme-scale applications.

• →Training and inference kernels produce different outputs in finite precision, creating systematic divergence independent of model weights.
• →The paper formalizes divergence as a chain of bounds from logit drift through total-variation distance to policy-gradient bias.
• →Kernel contracts provide contractual specifications combining numerical, statistical, runtime, and observability clauses with enforcement mechanisms.
• →The framework addresses reproducibility gaps between benchmark evaluation and production deployment of ML systems.
• →No production-scale empirical validation is reported; the contribution is primarily architectural and theoretical.