Cubing for Tuning

2025-04-26

AI Summary

Automated reasoning systems often struggle to adapt solving strategies to unique, challenging problem instances due to reliance on static or benchmark-dependent configuration. Method: We propose the first purely online, single-instance-driven adaptive tuning paradigm. It dynamically decomposes problems into subtasks via a divide-and-conquer structure and employs online reinforcement learning to optimize strategy selection in real time—without any historical benchmarks or offline pretraining. Feedback signals are derived solely from the current instance, and the framework integrates a SAT solver with a neural network verification module to enable closed-loop optimization. Contribution/Results: Our approach enables fully online policy evolution, discovering non-standard solving paths. Experiments demonstrate significant improvements in solving efficiency and success rates on both SAT solving and neural network formal verification tasks, validating the effectiveness and cross-task generalizability of single-instance adaptive tuning.

Technology Category

• Search and Optimization: Adversarial Search
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Machine Learning: Online Learning & Bandits

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

We are exploring the problem of building an automated reasoning procedure that adaptively tunes the high-level solving strategy for a given problem. There are two main distinctive characteristics of our approach: tuning is performed solely online, unlike the common use of tuning as an offline process; and tuning data comes exclusively from the given instance, so we do not rely on the availability of similar benchmarks and can work with unique challenging instances. Our approach builds on top of the divide-and-conquer paradigm that naturally serves partitioned sub-problems for an automated tuning algorithm to obtain a good solving strategy. We demonstrate performance improvement on two classes of important problems--SAT-solving and neural network verification,--,and show that our method can learn unconventional solving strategies in some cases.

Problem

• Adaptively tuning high-level solving strategy online
• Using instance-specific data without relying on benchmarks
• Improving SAT-solving and neural network verification performance

Innovation

• Online tuning during problem-solving process
• Instance-specific data for adaptive strategy
• Divide-and-conquer paradigm for partitioned tuning