Title: Look Back to Reason Forward: Revisitable Memory for Long-Context LLM Agents
Primary area: reinforcement learning
Keywords: LLM Agent, Reinforcement Learning, Long-Context LLM

Abstract: Large language models face challenges in long-context question answering, where key evidence of a query may be dispersed across millions of tokens.
Existing works equip large language models with a memory corpus that is dynamically updated during a single-pass document scan, also known as the "memorize while reading" methods.
While this approach scales efficiently, it suffers from irreversible forward-only processing, information loss through overwriting, and sparse reinforcement learning signals.
To tackle these challenges, we present ReMemR1, a memory-augmented agent with callback-enhanced memory that allows selective retrieval from the entire memory history and allows non-linear reasoning and revisiting of early evidence.
To further strengthen training, we propose Reinforcement Learning with Multi-Level Rewards (RLMLR), which combines final-answer rewards with dense, step-level signals that guide effective memory use.
Together, these contributions mitigate information degradation, improve supervision, and support multi-hop memory utilizing.
Experiments on long-document QA show significant gains over existing memory-based approaches, which validates ReMemR1 as an effective solution for long-context reasoning agents.