The study investigates how long‑video language models decide which frames to keep, compress, and reuse, testing each decision in isolation across six selection rules, three benchmarks, and two answering models. It finds that selecting frames based on queries yields the biggest performance boost, that halving spatial resolution costs little, and that reallocating saved tokens to more compressed frames can further improve accuracy. The work also highlights the importance of a unified evaluation harness to avoid misleading comparisons.
By Prakhar Khatri
arXiv:2610.14028v1 Announce Type: cross
Abstract: Data selection for LLM fine-tuning faces two facts about real training: data pools draw from many distinct domains, and evaluation spans diverse task...
By Luyang Zhang, Jialu Wang, Bebei Li
arXiv:2606. 03938v1 Announce Type: cross Abstract: Multi-epoch training is becoming the standard now that compute is growing faster than the supply of high-quality text.
By Bishwas Mandal, Shmuel Berman, Akshay Vegesna, Samip Dahal
arXiv:2608. 07719v1 Announce Type: new Abstract: Offline reinforcement learning repeatedly trains policies from a fixed transition pool, making redundant data costly across seeds and hyperparameters, while naive subsampling can remove rare transitions needed for long-horizon credit assignment.
By Ibne Farabi Shihab, Sanjeda Akter, Abu Sa-Adat Mohamed Moon-Im Al Ahsan, Md Najmus Swaqeeb, Anuj Sharma
The study investigates whether the usefulness of training samples depends on the data itself or on the learner. By fixing selected subsets and varying the learner, the authors show that the crossover point—where easy samples give way to coverage samples—shifts with changes in model width, input resolution, and architecture. This indicates that the boundary between easy and coverage samples is learner‑dependent rather than data‑dependent.
By Yangze Liu, Xiao-Long Yin, Zhongyi Han
LESS (Lightweight Evolutionary Supernet Search) is a data‑driven NAS method that uses a brief hard‑path warm‑up and CMA‑ES to evaluate candidate architectures as decoded hard genotypes after six supernet updates. On NAS‑Bench‑201, LESS attains 93.189 % CIFAR‑10 accuracy in just 409.1 seconds, nearly matching FairNAS while using only about 1/24 of its search time. The approach also transfers well to CIFAR‑100, ImageNet16‑120, and the larger DARTS space, achieving high accuracies with searches completed in roughly 43.5 minutes on a single GPU.
By Aviral Gandhi, Jinglue Xu, Jialong Li, Hitoshi Iba