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Trajectory Replay: Continuous-Time Mixture Transport for Lifelong Generative Models

Alessandro De Palma, Charles Jones, Tristan Cinquin, Fabian Theis, Frederik Wenkel · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26749 · cited by 0 works (OpenAlex) · open-access PDF

Portuguese

Adaptar modelos de geração de dados de tempo contínuo pré-treinados a dados de entrada sequencialmente sem esquecer tarefas anteriores é um desafio central na aprendizagem ao longo da vida. Uma estratégia líder, o replay gerativo, usa um modelo de geração treinado em tarefas históricas para aumentar sua própria atualização contínua com amostras sintéticas da experiência. No entanto, requer simulações de trajetória completa em cada etapa de treinamento, que acumulam erros de discretização e podem degradar a distribuição histórica reproduzida. Aqui, propomos o replay de trajetória, que contorna a geração de amostras treinando um campo de velocidade atualizado para combinar tanto uma velocidade histórica congelada ao longo de suas trajetórias quanto o campo de velocidade da tarefa de entrada, ponderado por um co

Machine translation (Meta M2M100); the English original follows.

Abstract

Adapting pre-trained continuous-time generative models to sequentially incoming data without forgetting earlier tasks is a central challenge in lifelong learning. A leading strategy, generative replay, uses a generative model trained on historical tasks to augment its own continual update with synthetic samples from experience. However, it requires full trajectory simulations at every training step, which accumulate discretization errors and can degrade the replayed historical distribution. Here, we propose trajectory replay, which bypasses sample generation by training an updated velocity field to match both a frozen historical velocity along its trajectories and the incoming task's velocity field, weighted by a stability-plasticity coefficient. Through the lens of continuous-time transport, we show the resulting optimum averages the two task velocities by their respective densities and generates the mixture of their probability paths. Combined with mean velocity distillation, trajectory replay accesses historical trajectories using only a few network evaluations per continual update. Empirically, it outperforms generative replay and distillation baselines on class-incremental image generation. In two biological case studies, trajectory replay extends generative perturbation models to new contexts and experimental batches across two data modalities while preserving treatment-effect prediction.

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Topics: Trajectory, Computer science, Generative grammar, Generative model, Artificial intelligence, Forgetting

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