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This paper presents a layer-wise information-theoretic framework for replay-based continual learning, decomposing the generalization gap into replay-induced representation drift and optimization-dependence terms, with refinements via Wasserstein relaxation and SGLD instantiation.
This paper introduces a novel continual learning framework for time series forecasting that uses attention-guided experience replay to enable models to adapt to new data distributions while avoiding catastrophic forgetting, evaluated on benchmarks and real-world piezometric data.
This paper adapts IndicTrans2-1B to conversational register across 21 Indic languages using experience replay and model souping, achieving conversational gains without sacrificing general-domain performance, though human evaluation shows the metric-based gains may not reflect perceived quality improvements.
CLaaS is a system for continual learning of LLM agents in deployment, using experience replay for sample-efficient online adaptation.
This paper investigates whether shallow neural network agents can master the card game Schnapsen using reinforcement learning, outperforming a supervised imitation baseline and achieving competitive results against a strong search-based opponent.
FreshPER introduces a freshness-aware prioritized experience replay method for LLM/VLM reinforcement learning that addresses the 'priority staleness' problem by applying exponential age decay to stored priorities, enabling off-policy reuse of trajectories. Evaluated on eight agentic, reasoning, and math tasks, FreshPER significantly outperforms on-policy baselines with gains up to +367% on Sokoban.
OpenAI presents Hindsight Experience Replay (HER), a technique enabling sample-efficient reinforcement learning from sparse binary rewards without complex reward engineering. It is demonstrated on robotic arm manipulation tasks including pushing, sliding, and pick-and-place, and validated on physical robots.