4 papers
Continual Test-Time Learning with Evolution Strategies
Guangya Hao, Yiting Duan, Longyan Tan +5
Large language models increasingly operate in open-ended environments, where they are expected to continually adapt to evolving tasks and distributions, often without access to ext…
ATTUNE: Agentic Throughput Tuning for LLM Training
Leo Fan, Hongyang Wang, Wenbo Chen +8
Training recipes are tuned by hand for one model, cluster and workload, and every new model, accelerator, framework version or dataset means tuning again. Automated tuners search a…
Radioactive Feedback: Tracing Models Trained by Label-Only LLM Supervision
Yuejia Li, JunHeng Li, Hao Wu +2
Conventional distillation transfers capability through teacher-generated demonstrations. LLM-as-a-Judge knowledge distillation offers a distinct and increasingly practical alternat…
Representation Drift in Continual Learning: LoRA versus N-gram Memory
Hao Wu
A language model that keeps learning online must not slowly erode its own base capabilities. We ask which part of a modern LLM should absorb continual updates, by measuring how eac…