Anytime Safe PAC Efficient Reasoning
This addresses efficiency and safety issues in online reasoning for AI systems, though it is an incremental improvement over existing selective thinking strategies.
The paper tackles the problem of high computational costs and latency in Large Reasoning Models by proposing Betting Probably Approximately Correct (B-PAC) reasoning, which reduces thinking model usage by up to 81.01% while controlling performance loss below a user-specified level.
Large Reasoning Models (LRMs) have demonstrated remarkable performance on complex tasks but suffer from high computational costs and latency. While selective thinking strategies improve efficiency by routing easy queries to non-thinking models, existing approaches often incur uncontrollable errors, especially in online settings where the performance loss of a non-thinking model is only partially observed and data are non-stationary. To address this, we propose Betting Probably Approximately Correct (B-PAC) reasoning, a principled method that enables anytime safe and efficient online reasoning under partial feedback. Specifically, we utilize inverse propensity scoring estimators to construct test supermartingales for candidate thresholds, and then dynamically adjust the routing threshold based on the accumulated statistical evidence of safety. Theoretically, we establish the anytime-valid performance loss control and the efficiency of B-PAC reasoning. Extensive experiments demonstrate that B-PAC reasoning significantly reduces computational overhead, decreasing thinking model usage by up to 81.01\%, while controlling the performance loss below the user-specified level.