LiFT: Loop Flow Transformers
- Published
- Source
- arXiv
- Paper number
- 1168
- Field
- Machine Learning
- arXiv ID
- 2610.05538
Key points
- It draws a straight path from the model's initial estimate to the target, and trains each loop to hit a point-specific goal along that path.
- The model keeps improving well beyond the number of loops it was trained with, so inference compute can be scaled freely at test time.
- On ImageNet 256x256 it cut FID by 3.34 points while using about 60% fewer parameters and about 52% less inference compute.
- It is a case of applying the test-time scaling idea to image generation at low cost.
Paper links
External research summaries. These are not HDATF publications or measured product results.