Author

Jun Zhao

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Preprint Aug 2026

SFT Conflicts, RL Coexists: A Theoretical and Empirical Analysis of Multi-Task Learning for LLMs

Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) exhibit fundamentally different behaviors in enhancing multi-task reasoning for large language models (LLMs). Our preliminary experiments revealed a phenomenon: SFT suffers from severe task conflicts under multi-stage training, whereas RL enables stable coexi...

Kejian Zhu, Zhuo-Ran Jin, Shangqing Tu et al. · 0 citations

Preprint Aug 2026

Quantization Degradation in Large Language Models: A Signal-Noise Perspective

This work systematically study weight-only post-training quantization across bit-widths, quantization methods, model scales and downstream tasks and establishes that quantization degradation is governed by how errors are introduced at the source and how they accumulate across the network.

Chen-Xi Zhou, Pengfei Cao, Jin Ye et al. · 0 citations