Pruning-Based Reinforcement Learning for Compression Operator Composition with Adjustable Objective Weights

Compression algorithm selection depends on application preferences over com- pression rate, compression time, and decompression time. We therefore use an adjustable weighted objective combining compression rate, compression time, and decompression time. Compression algorithms share operators that either reduce data size or transform data to facilitate compression. We identify and recombine these operators to seek a lower objective value than fixed operator pipelines. We propose operator-composition reinforcement learning (OCRL). It combines group relative policy optimization (GRPO) with pruning. The policy learns from mea- sured pipeline costs. A byte lower bound prunes branches that cannot improve the best verified candidate. OCRL recommends a compression operator pipeline for user-specified objective weights. It seeks a lower objective value than the com- pared baselines on real-world datasets.