Pruning low-advantage completions during RL improves F1 up to a middle pruning rate while reducing training time¶

Claim · All claims
Evidence · 1 study · design r2 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? As the RL pruning rate increases, F1 first improves then declines (staying above the no-pruning setting), while training time consistently decreases. → Zhiwei Yang 2026

Evidence¶

Zhiwei Yang 2026¶

Zhiwei Yang, Jiahua Yang, Huiru Lin, Xing Chen, and Quanlong Guan. (2026). SRJudge: Empowering Large Language Models with Selective Reasoning for Fine-Grained Knowledge Concept Tagging. arXiv preprint. https://arxiv.org/abs/2609.36982 doi:10.48550/arxiv.2609.36982

q2 · i? · design · r2

Numerical results across pruning-rate settings (Table 6): F1 peaks at the 0.50 pruning rate (0.7491 on S Math, 0.6925 on S Bio, 0.6629 on S Phy) and training time falls from 14.1 to 5.5 hours on S Math between rates 0.00 and 0.75. The article attributes this to filtering out low-quality completions with marginal advantages.

"As the pruning rate increases, F1 scores exhibit a trend of initial improvement followed by a decline (while exceeding the no-pruning setting). Meanwhile, training time consistently decreases as the pruning rate increases."

Discussion¶

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