arXiv:2605.21768v1 Announce Type: new Abstract: Memory-augmented LLM agents enable interactions that extend beyond finite context windows by storing, updating, and reusing information across sessions. However, training such agents with reinforcement learning in multi-session environments is challenging because memory turns the agent's past actions into part of its future environment. Once different rollouts write, update, or delete different memories, they no longer share the same intermediate memory state, making trajectory-level comparisons fundamentally unfair. This violates a key assumptio
This paper addresses a fundamental challenge in training memory-augmented LLM agents for multi-session environments, which is critical for their real-world deployment.
Fair credit assignment for long-horizon memory in AI agents is essential for developing robust and effective autonomous systems, unlocking more complex applications.
The proposed 'Memory-R2' technique offers a method to handle the non-stationary nature of memory in agent training, potentially accelerating the development of more sophisticated AI agents.
- · AI research labs
- · Developers of LLM agents
- · SaaS companies integrating agentic workflows
- · Companies relying on simpler, finite-context LLM interactions
Improved training methodologies for memory-augmented LLM agents.
Faster development and deployment of LLM agents capable of long-term, complex tasks.
Increased automation of white-collar tasks as agents become more reliable and capable.
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