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# Generative Replay

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  1. arXiv

    Robot policies ship as weights alone: letting a world action model replay its own generated past cuts forgetting by over 90%

    A world action model can regenerate complete rollouts of previously learned tasks from their instructions alone, and after truncation and physical-consistency filtering, replaying them cuts the forgetting of sequential fine-tuning by over 90% on four LIBERO suites and five real-world tasks without keeping any data from previous tasks.

  2. arXiv

    Compact domain footprints in a frozen embedding space enable generative replay for continual pathology report generation without storing slides or patch exemplars, outperforming exemplar-free and limited-buffer rehearsal baselines on multiple public continual learning benchmarks

    The work introduces an exemplar-free continual learning framework for whole-slide-image-to-report generation: it builds a compact domain footprint per domain in a frozen patch-embedding space (a k-means codebook, a slide-level code histogram bank, patch-count statistics, and a report-style prototype), uses it to synthesize pseudo-WSIs whose pseudo-reports come from an immediate teacher snapshot for generative replay, and conditions the language model through a style prefix; across multiple public continual learning benchmarks the approach outperforms exemplar-free and limited-buffer rehearsal baselines and supports domain-agnostic inference without explicit domain identifiers.

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