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We introduce ScaleLogic, a synthetic logical reasoning framework that offers independent control over two axes of difficulty: the depth of the required proof planning (i.e., the horizon) and the expressiveness of the underlying logic. Our proposed framework supports a wide range of logics: from simple implication-only logic (\"if-then\") towards more expressive first-order reasoning with conjunction (\"and\"), disjunction (\"or\"), negation (\"not\"), and universal quantification (\"for all\"). Using this framework, we show that the RL training compute $T$ follows a power law with respect to reasoning depth $D$ ($T \\propto D^{\\gamma}$, $R^{2} > 0.99$), and that the scaling exponent $\\gamma$ increases monotonically with l","title":"Can RL Teach Long-Horizon Reasoning to LLMs? 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