Find a story
Search Spins
Search titles, summaries, and missing voices across published articles — press releases, announcements, and media coverage.
3 results for “sample efficiency”
WMLLM: Self-Evolving Optimization Agents via Predict-Then-Act World Modeling
WMLLM is a new self-evolving optimization agent framework that uses large language models for world modeling to improve sample efficiency in black-box optimization, especially for multi-objective molecular design.
Sep 3, 2026
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control
Researchers introduced V-Simba, a new visual reinforcement learning architecture that improves sample efficiency and computational performance on standard robotics benchmarks without requiring algorithmic overhauls.
Aug 11, 2026
Neurosymbolic Reasoning with Incremental Knowledge for Sample Efficient Hierarchical Reinforcement Learning
A new neurosymbolic hierarchical reinforcement learning method called Incremental Knowledge (InK) improves sample efficiency in sparse-reward navigation tasks by enabling symbolic planning over updatable world knowledge, unlike fixed-knowledge HRL approaches.
Aug 5, 2026