arXiv:2605. 20804v2 Announce Type: replace-cross Abstract: We present a set of improvements to the OlmoEarth family.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
arXiv:2607. 16669v1 Announce Type: cross Abstract: OpenLanguageModel (OLM) is an open-source PyTorch library for building and pretraining small language models while keeping their machinery visible.
arXiv:2604. 15622v3 Announce Type: replace-cross Abstract: Always-on contextual AI runs language-aligned vision foundation models (VFMs) on edge devices, where the on-device model is the dominant continuous compute cost under strict latency and power limits.
The paper presents Puro-2B, an open-source language model pretraining recipe that enables training models up to 1.4 trillion tokens on consumer-grade RTX 5090 GPUs using FP8 precision. The authors achieve a best model with a compute cost under $6.9K, approaching Qwen2.5-1.5B performance, and introduce a Puro Cost Scaling Law indicating that about $4.4K suffices to match Qwen2-1.5B. Additionally, they analyze how pretraining data curricula affect downstream performance, providing a full training pipeline and releasing all resources under Apache 2.0.
arXiv:2609.14237v1 Announce Type: cross Abstract: LLM serving systems increasingly disaggregate inference into finer-grained stages, with recent approaches separating attention from FFN or MoE execut...
SIMPLER is a pre‑fine‑tuning method that reduces inference and deployment costs for Earth Observation foundation models by pruning redundant layers. It uses layer‑wise representation similarity on unlabeled task data to identify and remove up to 79% of parameters without requiring gradients, magnitude heuristics, or hyperparameter tuning. Experiments on Prithvi‑EO‑2, TerraMind, and ImageNet‑pretrained ViT‑MAE show that SIMPLER retains 94% of baseline performance while achieving 2.1× faster training and 2.6× faster inference.