Treating RL as a Learned Optimizer: Exploring Multi-Start Restart Strategies
YouJiacheng · x · 2026-08-04
Developer YouJiacheng discussed a new perspective on XM (likely referring to an RL/optimization algorithm): treating it as a learned optimizer.
He noted that using restart or multi-start strategies is a well-known practice in non-convex optimization, dating back to a 1982 paper. Consequently, it makes sense to train a learned optimizer with restart mechanisms. However, he also highlighted an unresolved challenge: how to effectively resolve the train-test mismatch from this perspective.
Related event: New Perspective on Model Training: Multi-Start Restart Strategies(3 posts)→
More from Research
- LeRobot now supports 30+ robot hardware integrations with drop-in plugins — m_wulfmeier · 2026-08-04
- Roasting Academia: NeurIPS is Essentially Just Scrolling OpenReview — abursuc · 2026-08-04
- Open-Source LoRA for Satellite Image Editing Uses AI to Auto-Generate Training Data — LimitlessSaint · 2026-08-04
- Weekend Project: RL-Trained 4B LLM Rewrites AI Text to Fool Open-Source Detectors — matthen2 · 2026-08-04
- NUS Creates Octopus-Inspired Swimming Robot Driven by Just Two Motors — lukas_m_ziegler · 2026-08-04
- Native Tool Calling and Correct Sampling Params Boost LLM Evals by >30 Points — xeophon · 2026-08-04