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• SARL (Structure Aware Reinforcement Learning) is a label-free framework that rewards the small-world topology of a Reasoning Map constructed from intermediate thinking steps.method 95%
• SARL surpasses ground truth based RL and prior label-free RL baselines on mathematical reasoning tasks, achieving an average gain of 9.1% under PPO and 11.6% under GRPO.result 90%
• SARL achieves substantial performance gains on open-ended reasoning tasks, with improvements of 34.6% under PPO and 30.4% under GRPO.result 90%
• The Structure Reward (SR) is composed of two terms: a Local Depth term measuring clustering coefficient (C(G)) and a Global Flow term measuring path efficiency (L(G)).technical 95%
• SARL exhibits lower KL divergence and higher policy entropy compared to baselines, indicating more stable and exploratory training.result 85%
• Unlike baseline methods EMPO and TTRL, which are restricted to GRPO-style training, SARL is compatible with both PPO and GRPO reinforcement learning frameworks.technical 90%
• Traditional RLVR methods are limited in open-ended domains where correctness is ambiguous and cannot be verified.limitation 85%
• The TTRL baseline method is not applicable for open-ended reasoning tasks because it requires guessing binary labels.limitation 90%

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