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SAT-ACT: Learning CDCL Branching from Outcome-Derived Action Preferences

arXiv cs.AI ↗ · 2026-11-02 Cached

SAT-ACT is a learning framework for CDCL branching that extracts action preferences from outcome-derived rollouts at the same solver state, trains a state-conditioned network to guide branching, and reduces search costs while improving PAR-2 on SAT Competition benchmarks.

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