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ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models
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we challenge prevailing assumptions by demonstrating that prolonged RL (ProRL) training can uncover novel reasoning strategies that are inaccessible to base models, even under extensive sampling.
Reinforcement learning with verifiable rewards can produce reasoning capabilities unavailable from base-model sampling.
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As quoted
we challenge prevailing assumptions by demonstrating that prolonged RL (ProRL) training can uncover novel reasoning strategies that are inaccessible to base models, even under extensive sampling.
Prolonged reinforcement learning training can enable models to solve tasks their base models cannot solve, regardless of sample count.
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- https://arxiv.org/abs/2505.24864
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