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cs.LG2025
Angles Don't Lie: Unlocking Training-Efficient RL Through the Model's Own Signals
Qinsi Wang, Jinghan Ke, Hancheng Ye +6
Current Reinforcement Fine-tuning (RFT) paradigms for Large Language Models (LLMs) suffer from sample inefficiency due to the redundant exposure of identical queries under uniform…
cs.LG2025
Dobi-SVD: Differentiable SVD for LLM Compression and Some New Perspectives
Qinsi Wang, Jinghan Ke, Masayoshi Tomizuka +3
We provide a new LLM-compression solution via SVD, unlocking new possibilities for LLM compression beyond quantization and pruning. We point out that the optimal use of SVD lies in…
cs.LG2023
MathNAS: If Blocks Have a Role in Mathematical Architecture Design
Wang Qinsi, Ke Jinghan, Liang Zhi +1
Neural Architecture Search (NAS) has emerged as a favoured method for unearthing effective neural architectures. Recent development of large models has intensified the demand for f…