Agentic SfM Training: Verifiable Tasks + Curriculum Learning for Controlled Difficulty
gabriberton · x · 2026-07-04
Researchers believe the key advantage of training MLLMs to perform 3D reconstruction using cropping, image matching, and COLMAP tools is task verifiability. Model outputs can be quantitatively evaluated, and samples can be sorted by difficulty to support curriculum learning, which significantly reduces training difficulty and costs.
More from Research
- Navier-Stokes, Riemann, P vs NP: what this week's math buzzwords mean for you — koltregaskes · 2026-09-11
- Fruit fly connectome LLM weights land on Hugging Face, transformers-compatible — ngxson · 2026-09-11
- Fruit fly brain as an LLM: connectome-driven language model demo goes live — ngxson · 2026-09-11
- Harry Collins: LLMs can't do frontier science because they can't invent new language — whoamisri · 2026-09-11
- The Waymo effect: how AI is quietly making research less collaborative — JohnHammersley · 2026-09-11
- Causal-only attention for non-generative tasks is wasteful, argues HF engineer — antoine_chaffin · 2026-09-11