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Life sciences · Preprint
arXiv · August 11, 2026
Posted before peer review. The findings may change or fail to hold.
This is a preprint in machine learning that proposes Critic-Free Pretraining (CFP), a methodological approach to offline-to-online reinforcement learning that removes critic reuse during pretraining. The work reports empirical results on benchmark tasks but has not undergone peer review and is not applicable to clinical practice or human health.
Preprint. Intervention: Critic-Free Pretraining (CFP): offline-to-online reinforcement learning paradigm that abandons offline critic training and uses a freshly initialized critic for online fine-tuning. Compared with: Conventional offline-to-online reinforcement learning algorithms.
CFP matches or improves conventional offline-to-online algorithms across diverse task sets CFP shows pronounced gains on several challenging tasks Method abandons offline critic training to allow fresh critic adaptation without inherited biased estimates
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This is an unrefereed preprint presenting a machine learning methodological contribution with algorithmic validation across benchmark tasks, not a clinical or biomedical study suitable for evidence grading.
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Offline-to-online (O2O) reinforcement learning aims to leverage policies pretrained on static datasets while improving them through online interaction. However, directly reusing an offline-trained critic can hinder online fine-tuning: as the policy and data distribution change rapidly, value estimates inherited from offline training may become misaligned with the online environment, leading to inaccurate policy improvement and inefficient exploration. To address this problem, we introduce \textbf{C}ritic-\textbf{F}ree \textbf{P}retraining: an efficient paradigm that completely abandons the approach of offline critic training, allowing a freshly initialized critic to adapt without inheriting biased estimates. CFP is compatible with various mainstream O2O algorithms and consistently matches or improves upon conventional O2O algorithms across a diverse set of tasks, with particularly pronounced gains on several challenging tasks.
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