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arXiv · 2510.02892

RoiRL: Efficient, Self-Supervised Reasoning with Offline Iterative Reinforcement Learning

Aleksei Arzhantsev·Otmane Sakhi·Flavian Vasile
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Abstract

Reinforcement learning (RL) is central to improving reasoning in large language models (LLMs) but typically requires ground-truth rewards. Test-Time Reinforcement Learning (TTRL) removes this need by using majority-vote rewards, but relies on heavy online RL and incurs substantial computational cost. We propose RoiRL: Reasoning with offline iterative Reinforcement Learning, a family of lightweight offline learning alternatives that can target the same regularized optimal policies. Unlike TTRL, RoiRL eliminates the need to maintain a reference model and instead optimizes weighted log-likelihood objectives, enabling stable training with significantly lower memory and compute requirements. Experimental results show that RoiRL trains to 2.5x faster and consistently outperforms TTRL on reasoning benchmarks, establishing a scalable path to self-improving LLMs without labels.

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Aleksei Arzhantsev, Otmane Sakhi, Flavian Vasile. 2025-10-03. RoiRL: Efficient, Self-Supervised Reasoning with Offline Iterative Reinforcement Learning. https://arxiv.org/abs/2510.02892

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