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cs.CL2026
Beyond Rejection Sampling: Trajectory Fusion for Scaling Mathematical Reasoning
Jie Deng, Hanshuang Tong, Jun Li +4
Large language models (LLMs) have made impressive strides in mathematical reasoning, often fine-tuned using rejection sampling that retains only correct reasoning trajectories. Whi…
cs.CL2026
ConPress: Learning Efficient Reasoning from Multi-Question Contextual Pressure
Jie Deng, Shining Liang, Jun Li +2
Large reasoning models (LRMs) typically solve reasoning-intensive tasks by generating long chain-of-thought (CoT) traces, leading to substantial inference overhead. We identify a r…