arXiv:2606.07581v1 Announce Type: new Abstract: A modern post-training pipeline often writes one symbol for its policy, pi_theta, while evaluating it through two different programs: a training kernel optimized for autograd and an inference kernel optimized for low-precision, fused, dynamically batched serving. In finite precision, these kernels can induce different distributions at identical weights, with the gap concentrated on slices that aggregate benchmarks under-represent. This paper proposes kernel contracts: a contract-first framework for specifying acceptable divergence between K_train
The increasing complexity of AI models and the critical need for reliable, performant deployment in real-world applications drive the imperative for rigorous divergence control between training and inference.
This work addresses a fundamental challenge in deploying AI safely and effectively, directly impacting the reliability and trustworthiness of AI systems as they become more integrated into critical infrastructure and decision-making processes.
By proposing 'kernel contracts,' this research introduces a formal framework to manage the crucial disparities between training and inference, potentially standardization how AI models are verified before deployment.
- · AI developers
- · High-stakes AI applications
- · AI safety researchers
- · Cloud AI providers
- · Ad-hoc AI deployment practices
- · AI systems with unquantified reliability
Increased reliability and predictability of AI models in production environments.
Faster and safer deployment of complex AI systems across various industries, including autonomous vehicles and industrial control.
Enhanced public trust in AI technologies, facilitating broader adoption and regulatory clarity, especially for agentic systems.
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Read at arXiv cs.LG