What Training Data Teaches RL Memory Agents: An Empirical Study of Curriculum Effects in Memory-Augmented QA
arXiv:2605.23067v1 Announce Type: new Abstract: Reinforcement learning (RL) has emerged as a viable recipe for training LLM agents to reason over external memory banks in multi-session dialogue. Existing work trains exclusively on a single benchmark, leaving open how the composition of training data shapes the skills a memory agent acquires. We present a controlled empirical study that holds architecture, RL algorithm, and all hyperparameters fixed and varies only the training curriculum across three conditions: in-domain (LoCoMo), mixed-benchmark (LoCoMo + LongMemEval), and out-of-domain (Lon
The rapid advancement of large language models (LLMs) and their integration into agentic systems necessitates a deeper understanding of how training data influences their memory and reasoning capabilities.
This empirical study provides critical insights into optimizing training curricula for memory-augmented RL agents, directly impacting the performance and reliability of future AI systems.
Understanding curriculum effects allows for more deliberate and efficient training strategies for AI agents, potentially leading to more robust and versatile autonomous systems across various applications.
- · AI researchers
- · Developers of intelligent agents
- · Companies investing in autonomous systems
- · Users of advanced AI applications
- · Developers relying on suboptimal training methods
- · Companies with inefficient AI model development cycles
Improved performance and reliability of memory-augmented RL agents in complex tasks like multi-session dialogue.
Accelerated development and deployment of more sophisticated AI agents capable of collapsing white-collar workflows.
Enhanced trust and adoption of AI agent technology across critical sectors due to increased robustness and understanding of their capabilities.
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