RaPO reduces catastrophic forgetting in visual continual learning by shaping rewards around policy drift and stabilizing advantages with cross-task exponential moving averages during reinforcement fine-tuning of multimodal models.
A comprehensive survey of continual learning: Theory, method and application.IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(8):5362–5383
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DISeL augments standard LoRA with per-input gates over rank-one updates to reduce catastrophic forgetting during fine-tuning while adding few parameters.
Fine-tuning a pop-pretrained Music Transformer on jazz recovers pop chord accuracy to baseline when mixing in about 2.5K pop samples alongside the jazz data.
citing papers explorer
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Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning
RaPO reduces catastrophic forgetting in visual continual learning by shaping rewards around policy drift and stabilizing advantages with cross-task exponential moving averages during reinforcement fine-tuning of multimodal models.
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Learning When to Adapt
DISeL augments standard LoRA with per-input gates over rank-one updates to reduce catastrophic forgetting during fine-tuning while adding few parameters.
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Empirical Study of Pop and Jazz Mix Ratios for Genre-Adaptive Chord Generation
Fine-tuning a pop-pretrained Music Transformer on jazz recovers pop chord accuracy to baseline when mixing in about 2.5K pop samples alongside the jazz data.