Towards robust long-context understanding of large language model via active recap learning

Abstract

In this paper, we propose active recap learning (ARL), a framework for enhancing large language model (LLM) in understanding long contexts. ARL enables models to revisit and summarize earlier content through targeted sequence construction during continued pretraining and retrospective summarization at inference. First, we identify key tokens in prepared long context based on loss gaps between long and short forward contexts and find the most relevant preceding paragraphs, then summarize them using an LLM. Second, ARL equips models with the ability to autonomously generate and utilize these retrospective summaries during inference, thereby establishing a recursive memory mechanism across paragraphs. Experimental results show substantial gains, with ARL achieving an improvement of up to 129.7\% on RULER and 11--13\% on LongBench. Overall, ARL offers a simple yet effective continued pretraining-based approach to strengthen long-context understanding, advancing scalable memory augmentation in LLM. Code is available at \href{https://github.com/nanfangxiansheng/TOWARDS-ROBUST-LONG-CONTEXT-UNDERSTANDING-OF-LARGE-LANGUAGE-MODEL-VIA-ACTIVE-RECAP-LEARNING}{CODE}