arXiv:2605.31273v1 Announce Type: new Abstract: While self-supervised Contrastive Reinforcement Learning (CRL) has shown remarkable depth-scaling capabilities, successfully using networks over 64 layers, scaled CRL still struggles with long-horizon goal-conditioned planning due to the uniformity-tolerance dilemma inherent in contrastive losses. We introduce Survival Reinforcement Learning (SRL), an online classification-based alternative that extends the survival value learning framework by maximizing the agent's dwell time at target goals. SRL bypasses the structural constraints of CRL and mi
The continuous push for more capable autonomous AI systems necessitates solving complex problems like long-horizon planning, which current methods struggle with.
Improved self-supervised reinforcement learning can unlock more sophisticated and autonomous AI agents, expanding their application scope and reducing reliance on human-labelled data.
A new method, Survival Reinforcement Learning, bypasses current limitations in contrastive learning, potentially leading to more scalable and robust AI planning capabilities.
- · AI developers
- · Robotics
- · Logistics
- · Autonomous systems
- · Companies reliant on human-labelled data for AI training
AI agents become more capable at long-term, goal-conditioned planning tasks.
This capability allows for higher levels of automation in complex, multi-step processes across various industries.
The increased autonomy of AI agents could reshape white-collar work and service industries by automating intricate decision-making and execution.
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Read at arXiv cs.LG