✨Cut Your Losses! Learning to Prune Paths Early for Efficient Parallel Reasoning

📝 Summary:
STOP is a systematic, learnable token-level path pruning method for Large Reasoning Models. It improves efficiency and accuracy, outperforming baselines and scaling across compute budgets to reduce futile paths in parallel reasoning.

🔹 Publication Date: Published on Apr 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.16029
• PDF: https://arxiv.org/pdf/2604.16029
• Project Page: https://bijiaxihh.github.io/STOP/
• Github: https://github.com/bijiaxihh/STOP

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#AI #LLM #MachineLearning #ParallelReasoning #ModelEfficiency