Reinforcement Learning

A new arXiv paper introduces Lighthouse RL, a reinforcement learning approach that restarts episodes from high-performing circuit configurations found during training. The goal is to reduce wasted exploration in sample-expensive analog circuit sizing.

A new arXiv paper proposes using the Lyapunov characteristic exponent as a physics-informed dense reward for stabilizing an inverted pendulum with vertical motion. The reported agent not only rediscovered Kapitza-like oscillatory stabilization but also damped the pivot motion into a strictly upright state.

A new arXiv paper introduces TRACE, a dense credit-assignment method for agentic reinforcement learning. It uses a frozen reference model to estimate state values and turn final-answer supervision into per-tool-call rewards.

A new arXiv paper introduces SIVA-RL, a framework that assigns visual-alignment supervision based on the observed effect of image interventions. The goal is to make vision-language models rely more reliably on visual evidence during reinforcement learning.

DAGR addresses a blind spot in goal-conditioned reinforcement learning: many goal embeddings do not account for the agent’s current state. The method improves navigation on OGBench, but its benefits do not consistently transfer to manipulation or puzzle tasks.