Categories: FAANG

Language Models Improve When Pretraining Data Matches Target Tasks

Every data selection method inherently has a target. In practice, these targets often emerge implicitly through benchmark-driven iteration: researchers develop selection strategies, train models, measure benchmark performance, then refine accordingly. This raises a natural question: what happens when we make this optimization explicit? To explore this, we propose benchmark-targeted ranking (BETR), a simple method that selects pretraining documents based on similarity to benchmark training examples. BETR embeds benchmark examples and a sample of pretraining documents in a shared space, scores…
AI Generated Robotic Content

Recent Posts

Chroma Radiance, Mid training but the most aesthetic model already imo

submitted by /u/Different_Fix_2217 [link] [comments]

2 hours ago

From human clicks to machine intent: Preparing the web for agentic AI

For three decades, the web has been designed with one audience in mind: People. Pages…

3 hours ago

Best GoPro Camera (2025): Compact, Budget, Accessories

You’re an action hero, and you need a camera to match. We guide you through…

3 hours ago

What tools would you use to make morphing videos like this?

submitted by /u/nikitagent [link] [comments]

1 day ago

Bias after Prompting: Persistent Discrimination in Large Language Models

A dangerous assumption that can be made from prior work on the bias transfer hypothesis…

1 day ago

Post-Training Generative Recommenders with Advantage-Weighted Supervised Finetuning

Author: Keertana Chidambaram, Qiuling Xu, Ko-Jen Hsiao, Moumita Bhattacharya(*The work was done when Keertana interned…

1 day ago