Segment‑Level Adaptive Trimming Halves CoT Length Without Accuracy Loss
SLAT cuts chain‑of‑thought length in half while preserving answer quality, using a segment‑wise reinforcement‑learning trim that discards low‑utility reasoning steps. Experiments on standard benchmarks show a 50% reduction in computation with comparable accuracy, offering a more efficient path for large reasoning models.