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cs.LG2026
Memorize Theorems, Not Instances: Probing SFT Generalization through Mathematical Reasoning
Ruiying Peng, Mengyu Yang, Jing Lei +3
Supervised Fine-Tuning (SFT) is widely used for task-specific adaptation, yet recent work shows it systematically undermines reasoning generalization. We argue the root cause is no…
cs.LG2024
When Can Proxies Improve the Sample Complexity of Preference Learning?
Yuchen Zhu, Daniel Augusto de Souza, Zhengyan Shi +4
We address the problem of reward hacking, where maximising a proxy reward does not necessarily increase the true reward. This is a key concern for Large Language Models (LLMs), as…
cs.LG2024
Causal Fine-Tuning under Latent Confounded Shift
Jialin Yu, Yuxiang Zhou, Haoxuan Li +6
Adapting to latent confounded shift remains a core challenge in modern AI. This setting is driven by hidden variables that induce spurious correlations between inputs and outputs d…