SPAARS: Safer RL Policy Alignment through Abstract Exploration and Refined Exploitation of Action Space

Offline-to-online reinforcement learning (RL) offers a promising paradigm for robotics by pre-training policies on safe, offline demonstrations and fine-tuning them via online interaction. However, a fundamental challenge remains: how to safely explore online without deviating from the behavioral support of the offline data? While recent methods leverage conditional variational autoencoders (CVAEs) to bound exploration within a latent space, they inherently suffer from an exploitation gap -- a p

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ethics.ai (10 March 2026), “SPAARS: Safer RL Policy Alignment through Abstract Exploration and Refined Exploitation of Action Space,” evidence record 7441, https://ethics.ai/record/7441 (originally published by arXiv).

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