Large language models (LLMs) face catastrophic forgetting and plasticity loss when updating parameters for downstream tasks. This work introduces a fast-slow learning framework for LLMs, utilizing model parameters as "slow" weights and optimized context as "fast" weights to adapt efficiently without compromising general reasoning.
This paper proposes 'memorized mistake-gated learning,' a biologically plausible plasticity rule where synaptic updates are strictly gated by current and past classification errors. This method reduces network updates by 50-80%, significantly enhancing energy and memory efficiency in continual and online learning scenarios.
This research introduces Adaptive Memory Crystallization (AMC), a novel memory architecture designed for autonomous AI agents to progressively consolidate experiences in dynamic environments without forgetting prior knowledge. AMC models memory as a continuous crystallization process across a three-phase hierarchy, inspired by synaptic tagging and capture theory and governed by stochastic differential equations.
Adaptive Hierarchical Compression (AHC) is a meta-learning framework for continual object detection on memory-constrained microcontrollers, adapting to evolving task distributions. It employs MAML-based adaptive compression, hierarchical multi-scale compression, and a dual-memory architecture to prevent catastrophic forgetting within a strict 100KB memory budget.
This position paper argues that deployed Reinforcement Learning (RL) agents should engage in continual learning rather than a train-then-fix paradigm. It identifies four sources of non-stationarity post-deployment, highlighting the necessity for agents to continuously adapt to achieve optimal performance in real-world scenarios.
Large language models often hallucinate facts, a problem exacerbated by supervised fine-tuning (SFT) which degrades pre-trained knowledge. This research proposes a self-distillation SFT method, inspired by continual learning, to mitigate hallucinations by regularizing output-distribution drift while effectively acquiring new factual information.
DeepMind CEO Demis Hassabis predicts AGI could arrive around 2030, identifying three critical missing pieces in current AI: continual learning, long-term reasoning, and real memory. He describes today's models as exhibiting "jagged intelligence," with strong peaks alongside brittle failures.
The title "Deep Generative Dual Memory Network for Continual Learning" describes a deep generative neural network architecture. It aims to enable continual learning, allowing the model to acquire new information without forgetting previously learned knowledge, by employing a dual memory approach.
The author realized the critical need for AI in endangered language preservation, encountering challenges like catastrophic forgetting in neural machine translation systems and complex multi-jurisdictional data sovereignty laws. The work focuses on meta-optimized continual adaptation for heritage language revitalization programs.
This content likely explores the concept of continual learning, a key challenge in AI, by leveraging neural pruning techniques. It aims to demonstrate how pruning can enable models to sequentially acquire new knowledge without forgetting previously learned information.
This content introduces novel metrics for Continual Learning, broadening evaluation beyond just preventing catastrophic forgetting. It proposes a more comprehensive view for measuring AI model performance in sequential learning scenarios.
The paper proposes NORACL, inspired by biological neurogenesis, to address the stability-plasticity dilemma in continual learning. It tackles the oracle architecture problem, where finite networks have limited resources for unknown future tasks.
This study investigates the role of external memory in LLM agents for continual learning, showing that the stability-plasticity dilemma resurfaces at the memory level due to limited context windows. A (k,v) framework is introduced to disentangle how experience is represented and organized, finding that abstract procedural memories transfer more reliably than detailed trajectories and finer-grained memory organization is beneficial.
CP-MoE addresses catastrophic forgetting in continual learning for LLMs and VLMs using Mixture-of-Experts architectures. It introduces a transient expert and consistency-preserving routing to integrate new knowledge while preventing the overwriting of existing parameters.
This research introduces Continual Distillation (CD), a new paradigm where a student model sequentially learns from a stream of teacher models without retaining prior access. It addresses challenges like unseen knowledge transfer (UKT) and forgetting (UKF) through Self External Data Distillation (SE2D), which uses external unlabeled data to stabilize learning across heterogeneous teachers.
This paper introduces Deployment-Time Learning (DTL) as a new stage for LLMs, allowing them to continually adapt from experience post-training without modifying core parameters. It presents CASCADE, a framework that uses an explicit, evolving episodic memory for LLM agents, formalizing experience reuse as a contextual bandit problem with no-regret guarantees.
This paper proposes a new lightweight selector to capture logit shift trends in Continual Learning (CL), a computationally expensive challenge in pre-trained model selection. The research addresses architectural heterogeneity in neural networks by decoupling architecture and data dependency to establish a new theoretical framework.
Este artigo investiga sistematicamente as dinâmicas de aprendizado do Pré-treinamento Contínuo de Contexto Longo (LCCP) usando o modelo industrial Hunyuan-A13B, rastreando sua evolução por 200 bilhões de tokens. Ele propõe uma estrutura hierárquica para analisar o LCCP em níveis comportamental, probabilístico e mecanicista, abordando as limitações das metodologias atuais de avaliação e pré-treinamento.
LangChain Labs is a new applied research effort focused on continual learning for agents. It aims, with partners, to advance open research on self-improving AI systems.
This content discusses continual learning for AI agents, proposing that learning extends beyond just updating model weights. It introduces three distinct layers where learning can occur – the model, the harness, and the context – emphasizing how this perspective changes the approach to building improving AI systems.
