3 papers
cs.LG2026
Sequential Data Poisoning in LLM Post-Training
Jack Sanderson, Yihan Wang, Xiaoqian Lu +2
LLM post-training proceeds through multiple stages, e.g., supervised fine-tuning (SFT) followed by reinforcement learning from human feedback (RLHF) or direct preference optimizati…
cs.AI2026
Iterative Finetuning is Mostly Idempotent
Zephaniah Roe, Jack Sanderson, Dang Nguyen +5
If a model has some behavioral tendency, such as sycophancy or misalignment, and it is trained on its own outputs, will the tendency be amplified in the next generation of models?…
cs.LG2025
Rethinking LLM Advancement: Compute-Dependent and Independent Paths to Progress
Jack Sanderson, Teddy Foley, Spencer Guo +2
Regulatory efforts to govern large language model (LLM) development have predominantly focused on restricting access to high-performance computational resources. This study evaluat…