Xun Sun, Thanh Thi Nguyen, Thuy-Trang Vu · 2026 · arXiv
DOI: 10.48550/arXiv.2610.31401 · open-access PDF
継続的な学習には、既存の能力を損なうことなく新しいスキルや知識を継続的に獲得するためのモデルが必要です パラメトリック・メソッドは、災害的な忘却とプラスティック性の喪失に苦しんでいます プロンプト、スキル、ハーネスなどの非パラメトリック・メソッドは、文脈内の学習を活用しますが、文脈の長さと失われた単純テキストの圧縮によって制限されています 効果的にその補完的な強みを組み合わせるために、我々はHERD(Harness-Evolving Recursive Distillation)を提案します、ハーネスで効率的に学習し、経験を累積的に内部化するハイブリッドの継続的な学習枠組み
Machine translation (Meta M2M100); the English original follows.
Continual learning requires a model to keep acquiring new skills and knowledge without compromising its existing capabilities. Parametric methods suffer from catastrophic forgetting and loss of plasticity. Non-parametric methods, such as prompts, skills, and harnesses, leverage in-context learning but are limited by context length and lossy plain-text compression. To effectively combine their complementary strengths, we propose HERD (Harness-Evolving Recursive Distillation), a hybrid continual learning framework that learns efficiently in the harness and internalizes experience into cumulative
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