arXiv:2508.06659v2 Announce Type: replace Abstract: Reinforcement learning (RL) agents often struggle to generalize to new tasks and contexts without updating their parameters, mainly because their learned representations and policies are overfit to the specifics of their training environments. To boost agents' in-context RL (ICRL) ability, this work formulates ICRL as a two-agent emergent communication problem and introduces CORAL (Communicative Representation for Adaptive RL), a framework that learns a transferable communicative context by functionally separating latent representation learni
The paper addresses a core limitation of current reinforcement learning (RL) agents – their inability to generalize quickly to new tasks without extensive retraining – which is a major bottleneck for advanced autonomous systems.
Improving in-context reinforcement learning (ICRL) through communicative AI frameworks could unlock more adaptable and efficient AI agents, accelerating their deployment in complex real-world scenarios.
AI agents may soon be capable of learning and adapting to novel environments and tasks significantly faster, requiring fewer parameter updates and less task-specific training data.
- · AI Agents developers
- · Robotics companies
- · Automation sector
- · Companies seeking adaptable AI solutions
- · Companies reliant on highly specialized, non-generalizable AI models
- · Labor performing highly repetitive, context-transferable tasks
More robust and generalizable AI models become available for research and commercial applications.
Accelerated development and deployment of autonomous AI agents across various industries, from logistics to customer service.
The definition of 'task' for AI agents expands, potentially enabling them to take on increasingly abstract and open-ended goals.
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