做推理模型微调或部署的团队，STOP 解决了低数据场景下长链推理成本高的问题，直接减少 token 消耗，建议试试这个轻量剪枝方案。
长链推理（Long CoT）虽能提升多步推理性能，但常导致模型过度思考，产生低效推理，增加推理成本。STOP 提出一种结构化在线策略剪枝算法，通过自蒸馏、节点分割和推理树构建，识别并保留最早的正确推理节点（ECN），去除冗余推理。在 DeepSeek-R1-Distill-Qwen-7B 等模型上，STOP 在低数据微调场景下减少 19.4%-42.4% 的生成 token，同时基本保持准确率。该方法比教师引导剪枝带来更小的分布偏移，并将推理努力从冗余验证转向更高效的探索。
STOP: Structured On-Policy Pruning of Long-Form Reasoning in Low-Data Regimes
Long chain-of-thought (Long CoT) reasoning improves performance on multi-step problems, but it also induces overthinking: models often generate low-yield reasoning that increases inference cost and latency. This inefficiency is especially problematic in low-data fine-tuning regimes, where real applications adapt reasoning models with limited supervision and cannot rely on large-scale teacher distillation or heavy test-time control. To address this, we propose STOP (Structured On-policy Pruning), an on-policy algorithm for analyzing and pruning long-form reasoning traces. STOP constructs self-distilled traces from the model. Then it maps each trace into a structured reasoning interface through node segmentation, taxonomy annotation, and reasoning-tree construction. On top of this interface, we introduce ECN (Earliest Correct Node), which retains the shortest prefix ending at the earliest node that both functions as an answering conclusion and yields the correct final answer, removing redundant post-solution reasoning while preserving semantic continuity. Experiments on DeepSeek-R1-Distill-Qwen-7B and DeepSeek-R1-Distill-LLaMA-3-8B across GSM8K, Math 500, and AIME 2024 show that STOP reduces generated tokens by 19.4-42.4% while largely preserving accuracy in low-data fine-tuning. Beyond efficiency, our analyses show that STOP induces much smaller distributional shift than teacher-guided pruning, improves the structural efficiency of generated reasoning, and reallocates reasoning effort away from redundant verification and backtracking toward more productive exploration.