I2T loss stays comparable across tokenizers and predicts image generation quality
peterxichen · x · 2026-09-15
From a training research thread (part 6/9): image-to-text (I2T) loss likely reflects overall joint training progress. Since every model predicts within the same text token space, I2T loss remains comparable across tokenizers, whereas text-to-image loss does not.
Key finding: I2T loss stays informative after SFT, correlating with both image generation quality and general VQA performance—making it a cross-model yardstick for multimodal training progress.
More from Research
- ORQA paper tests LLM knowledge across 116 occupations; top models score just ~60% — soumitrashukla9 · 2026-09-15
- Hand-Deriving the VAE in 11 Steps: One Diagram Teaches KL Divergence and Diffusion Loss — ProfTomYeh · 2026-09-15
- Fly connectome reveals fast-weight continual learning neurons — a skill current LLMs lack — tszzl · 2026-09-15
- Hyperstition claims 62% pretraining cost cut and 1.7B math model beating Qwen3 — nick_linck · 2026-09-15
- Biologist Eörs Szathmáry warns AGI's replication speed makes it a runaway biosphere risk — danfaggella · 2026-09-15
- Survey: Asian AI researchers worry more about AI risks than Western peers — KatjaGrace · 2026-09-15