arXiv:2509.06810v3 Announce Type: replace-cross Abstract: Humans can uniquely assign value to novel, abstract outcomes to support reinforcement learning. However, this flexibility is cognitively costly and reduces learning efficiency. We propose that goal-dependent learning initially relies on capacity-limited working memory. With consistent experience, learners create a "compressed" reward function - a simplified goal rule -- that transfers to long-term memory for a more automatic evaluation upon receiving feedback. This automaticity frees working memory resources, thereby boosting learning e
This research explores a fundamental cognitive mechanism for efficient learning, directly addressing a core challenge in current AI development — the need for more efficient and adaptable learning algorithms.
Understanding and emulating how biological systems compress reward functions can lead to significant breakthroughs in AI efficiency, reducing computational costs and accelerating agent learning in complex environments.
Current AI models often require extensive data and re-training for new goals; this research suggests a path towards more flexible, goal-dependent learning that mirrors human cognitive processes.
- · AI researchers
- · Reinforcement learning developers
- · Robotics
- · Cognitive science
- · AI models reliant on brute-force training
More efficient and adaptable AI agents become possible, requiring less computational power and data for skill acquisition.
This could accelerate the development of autonomous AI systems capable of operating in dynamic, unpredictable environments with novel challenges.
Advanced AI agents, learning more like humans, could lead to unforeseen applications across various industries, from scientific discovery to complex decision-making.
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