RandOpt: Hypothesis-Ensemble-Distillation Iteration for 1-Pass High-Performance Models
phillip_isola · x · 2026-07-08
Phillip Isola clarifies two common questions about RandOpt: repeating the process yields continuous improvements. He outlines a "hypothesis → ensemble → distillation → repeat" pipeline that ultimately produces a highly capable 1-pass inference model. The code is open-source. This serves as the core methodological explanation for his ICML research.
Related event: RandOpt: Achieving High Performance via Iterative Distillation(2 posts)→
More from Research
- Reddit asks whether LLMs need a benchmark for treasure-hunt style reasoning — StrangeOops · 2026-07-21
- Open-source LangGraph coding agent only submits patches after tests pass — wusuiling-if · 2026-07-21
- A Markov-chain note explores using eigenvectors to speed up mixing — michaelchchoi · 2026-07-21
- Xiaomi-Robotics-1 shows robot motion improves more from data than bigger models — The Decoder · 2026-07-21
- FloC 2026 AIMACS workshop on AI for math and CS set for July 25 — swarat · 2026-07-21
- Knowledgeless Language Models cut closed-book recall by anonymizing entities during pretraining — gdm3000 · 2026-07-21