arXiv:2511.00802v2 Announce Type: replace-cross Abstract: With data-driven development now widely adopted, online A/B testing is an established method for measuring the effects of new technologies. However, deploying online experiments demands resources for design, implementation, and deployment, and may negatively impact users (e.g., unsafe or unethical outcomes) while requiring weeks of data collection. To address this, the growing research area of off-policy evaluation (OPE), or offline A/B testing, assesses new technologies offline using previously collected logged data. OPE is also a fund
The increasing complexity of data-driven development and the cost/risk of online experimentation are driving innovations in offline evaluation methods, supported by advancements in LLMs.
Automated off-policy evaluation, especially with LLM agents, accelerates the development and deployment of new technologies by reducing the need for costly and risky A/B testing while optimizing performance.
The ability to rapidly and autonomously optimize technology deployment using offline data and code-modifying LLMs fundamentally changes the development cycle for data-driven systems.
- · AI/ML development teams
- · Companies using data-driven products
- · Traditional A/B testing platforms
Faster iteration and deployment of more effective AI models without extensive online testing.
Increased competition due to accelerated innovation cycles and reduced barriers to new technology adoption.
Enhanced overall quality and safety of deployed AI systems as optimization becomes more robust and immediate.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG