  Boolean satisfiability (SAT) is a fundamental NP-complete problem with many
applications, including automated planning and scheduling. To solve large
instances, SAT solvers have to rely on heuristics, e.g., choosing a branching
variable in DPLL and CDCL solvers. Such heuristics can be improved with machine
learning (ML) models; they can reduce the number of steps but usually hinder
the running time because useful models are relatively large and slow. We
suggest the strategy of making a few initial steps with a trained ML model and
then releasing control to classical heuristics; this simplifies cold start for
SAT solving and can decrease both the number of steps and overall runtime, but
requires a separate decision of when to release control to the solver.
Moreover, we introduce a modification of Graph-Q-SAT tailored to SAT problems
converted from other domains, e.g., open shop scheduling problems. We validate
the feasibility of our approach with random and industrial SAT problems.
