AINeutralarXiv – CS AI · May 276/10
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Reasoning Depth and Environment Complexity: A Controlled Study of RLVR Data Allocation across Logical Reasoning Tasks
Researchers conducted a controlled study on reinforcement learning with verifiable rewards (RLVR) for reasoning models, revealing that training data allocation across multiple reasoning dimensions—depth, environment complexity, and reasoning types—significantly impacts model performance. The study found that joint coverage of these dimensions outperforms single-axis training approaches, and that models exhibit systematic weaknesses in abductive reasoning regardless of training setup.