In reinforcement frameworks, the lumps of every reinforcement are physically scattered after de-duplication, which causes a testing fracture issue. We watch that the discontinuity comes into inadequate and out-of-arrange compartments. The inadequate holder diminishes reestablish execution and rubbish gathering proficiency, while the out-of-arrange compartment diminishes reestablish execution if the reestablish store is little. Keeping in mind the end goal to lessen the discontinuity, we propose History-Aware Rewriting calculation (HAR) and Cache-Aware Filter (CAF). HAR abuses chronicled data in reinforcement frameworks to precisely distinguish and lessen meager holders, and CAF misuses reestablish store learning to recognize the out-of-arrange compartments that hurt reestablish execution. CAF productively supplements HAR in datasets where out-of-arrange compartments are predominant. To lessen the metadata overhead of the refuse gathering, we additionally propose a Container-Marker Algorithm (CMA) to recognize substantial compartments rather than legitimate pieces. Our broad test comes about because of genuine datasets indicate HAR essentially enhances the reestablish execution by 2.84-175.36 at a cost of just revamping 0.5-2.03% information