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cs.LG2025
RL Is Neither a Panacea Nor a Mirage: Understanding Supervised vs. Reinforcement Learning Fine-Tuning for LLMs
Hangzhan Jin, Sicheng Lv, Sifan Wu +1
Training large language models (LLMs) from scratch is increasingly impractical, making post-training methods such as supervised fine-tuning (SFT) and reinforcement-learning fine-tu…
cs.AI2025
What to Ask Next? Probing the Imaginative Reasoning of LLMs with TurtleSoup Puzzles
Mengtao Zhou, Sifan Wu, Huan Zhang +2
We investigate the capacity of Large Language Models (LLMs) for imaginative reasoning--the proactive construction, testing, and revision of hypotheses in information-sparse environ…
cs.CE2025
Seeing Beyond Words: MatVQA for Challenging Visual-Scientific Reasoning in Materials Science
Sifan Wu, Huan Zhang, Yizhan Li +3
The emergence of Multimodal Large Language Models (MLLMs) that integrate vision and language modalities has unlocked new potentials for scientific reasoning, outperforming prior be…