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arXiv AI By Yihua Zhu, Qianying Liu, Fei Cheng, Jiaxin Wang, Akiko Aizawa, Sadao Kurohashi, Hidetoshi Shimodaira

Reasoning Depth and Environment Complexity: A Controlled Study of RLVR Data Allocation across Logical Reasoning Tasks

Read the original on arXiv AI →

The paper investigates reinforcement learning with verifiable rewards (RLVR) by expanding the reasoning space beyond depth to include environment complexity and diverse reasoning forms. It introduces a synthetic knowledge‑graph environment that varies depth, complexity, and task family, revealing that joint depth‑complexity coverage outperforms single‑axis approaches, that different reasoning families behave non‑uniformly, and that uniform mixing beats staged curricula under a fixed budget. The study also shows that current off‑the‑shelf models share a deductive‑over‑abductive bias, indicating a broader gap in reasoning capabilities.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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