HybridAL cuts active learning training time by switching retraining to fine-tuning on stabilization signals
Technion · hf · 2026-09-10
HybridAL adaptively switches from full retraining to fine-tuning during active learning based on online stabilization signals, reducing training time while preserving accuracy and improving calibration.
More from Research
- Why winning training recipes at proxy scale can lose at target scale — gerardsans · 2026-09-10
- 3Blue1Brown's visual explainer of how LLMs work resurfaces — blaizedsouza · 2026-09-10
- AI-Designed Drug Rentosertib Shifts Six Aging Clocks in Phase IIa Trial — AIFlow_ML · 2026-09-10
- Bug Hunt Bench grades frontier models on 105 real bugs; DeepSeek-V4.1-Flash lands 24/105 for $1.80 — PawelHuryn · 2026-09-10
- TUM's PlannerForge Uses LLM Agents to Automate Scenario-Based Testing of Autonomous Driving Motion Planners — TUM-AVS · 2026-09-10
- AgentGrad Targets the Right Agent First: Intervention-Guided Prompt Optimization for Multi-Agent Systems — Jaewon Chu · 2026-09-10