Dimensions overall score 9.0

• ARISE consistently outperforms GRPO-family algorithms and memory-augmented baselines on seven benchmarks spanning competition mathematics and Omni-MATH.result 95%
• ARISE achieves particularly notable performance gains on out-of-distribution tasks compared to baseline methods.result 90%
• ARISE uses a hierarchical reinforcement learning framework with a shared policy that operates as both a Skills Manager and a Worker.method 98%
• The Manager in ARISE maintains a tiered skill library through structured summarization of successful solution traces after execution.method 95%
• ARISE employs a policy-driven selection mechanism to retrieve relevant skills to condition future rollouts before execution.method 95%
• Ablation studies confirm that each component of ARISE contributes to the observed improvements.result 90%
• Library quality and reasoning performance improve in tandem throughout ARISE training.result 90%
• ARISE requires verifiable rewards, limiting its use to domains with clear correctness metrics.limitation 85%
• ARISE is a hierarchical reinforcement learning framework that manages skills at a high level and generates responses at a low level.method 70%
• ARISE consistently outperforms GRPO-family algorithms and memory-augmented baselines on mathematical reasoning tasks.result 70%
• ARISE shows notable gains on out-of-distribution tasks.result 70%
• Each component of ARISE contributes to the observed improvements.method 70%

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