Attention Variants and Memory Efficiency

Overview This post is divided into eight parts; they are: • The Checkpoint Fixes the Attention Architecture • A Closer Look at Attention Memory • Multi-Head Attention • Multi-Query and Grouped-Query Attention • Sliding-Window and Mixed Attention • Multi-Head Latent Attention • What the Inference Runtime Can Change • From Cache Size to Serving Capacity …

BAIR at CoRL 2026

The Conference on Robot Learning (CoRL) takes place November 9–12, 2026, in Austin, Texas. BAIR faculty, postdocs and students will be presenting research at various sessions, including an Early Career Keynote from Professor Negar Mehr. See below for the full schedule of sessions with BAIR members, including papers co-authored with collaborators at other universities and …

Floating-Point Precision and Its Effects to Model Inference

Overview This post is divided into five parts; they are: • How Floating-Point Numbers Work • Common Precision Formats • Loading a Model at a Different Precision • Mixed Precision and Sensitive Operations • Measuring the Result How Floating-Point Numbers Work A floating-point number contains a sign, an exponent, and a significand (often called the …