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arXiv · 2605.31273

Survival Reinforcement Learning: Toward Scalable Self-Supervised RL

Franki Nguimatsia-Tiofack·Fabian Schramm·Théotime Le Hellard·Justin Carpentier
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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 mitigates the "bang-bang" control solutions inherent to survival frameworks, which often induce undesirable behavior in complex dynamical systems. Evaluated across diverse robotic benchmarks, scaled SRL matches state-of-the-art CRL on manipulation tasks and outperforms it by 2x to 8x on stable, long-horizon locomotion tasks. Our results provide strong additional evidence that classification-based methods may serve as a key primitive in the broader effort to scale reinforcement learning and an open-source implementation is available online: https://github.com/Simple-Robotics/survival-reinforcement-learning.

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Franki Nguimatsia-Tiofack, Fabian Schramm, Théotime Le Hellard, Justin Carpentier. 2026-09-18. Survival Reinforcement Learning: Toward Scalable Self-Supervised RL. https://arxiv.org/abs/2605.31273

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