  LambdaBeam is a state-of-the-art execution-guided algorithm for program
synthesis that incorporates higher-order functions, lambda functions, and
iterative loops into the Domain-Specific Language (DSL). LambdaBeam generates
every program from the start. Yet, many program blocks or subprograms occur
frequently in a given domain, e.g., loops to traverse a list. Thus, repeating
programs can be used to enhance the synthesis algorithm. However, LambdaBeam
fails to leverage this potential. For this purpose, we introduce AbstractBeam:
A novel program synthesis framework that employs Library Learning to identify
such program repetitions, integrates them into the DSL, and thus utilizes their
potential to boost LambdaBeam's synthesis algorithm. Our experimental
evaluations demonstrate that AbstractBeam significantly improves LambdaBeam's
performance in the LambdaBeam integer list manipulation domain. Additionally,
AbstractBeam's program generation is more efficient compared to LambdaBeam's
synthesis. Finally, our findings indicate that Library Learning is effective in
domains not specifically crafted to highlight its benefits.
