Tag
This paper identifies the retention-forgetting dilemma in verbal reinforcement learning for LLM agents operating in non-stationary environments, and proposes a three-layer architecture with a feedback-driven curation loop to govern insight extraction and application.
This paper investigates the stability-plasticity dilemma in reinforcement learning under gradual non-stationarity, finding that stabilizing successor features via synaptic consolidation across multiple timescales outperforms plasticity-focused methods.
The paper introduces CXR-MAX, a large-scale benchmark for evaluating reasoning alignment in non-stationary environments using X-ray data from multiple MLLMs.