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SwiftExplorer is proposed, a plugin that mitigates distribution collapse caused by excessive diversity loss and reduces compute costs, and adopts an Inheritance-Restart exploration mechanism to avoid early convergence, while exploration also increases the likelihood of high-reward trajectories.
A Density-Aware Regional Decisive replay strategy, termed InfoDense, which effectively mitigates catastrophic forgetting while improving cross-domain generalization and knowledge retention and generalization.
Stage-Guided Per-Step Optimization (SGPO) is proposed for diffusion models, which jointly leverages signal-to-noise ratio and semantic changes to identify generation stages and adaptively assign stage-specific objectives.
PAST is proposed, which provides differentiated rewards while adaptively regulating training episode length by jointly perceiving denoising progress and prompt difficulty and establishes a dual adaptive coordination mechanism that balances the extrinsic and intrinsic rewards.
Latent diffusion models (LDMs) enable efficient high-resolution image synthesis by denoising in a VAE-compressed latent space. However, fixed visual tokenizers can discard fine textures and structural details, while separate representation and diffusion training creates a mismatch between reconstruction and generation...
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