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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.
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.
HEMERA is presented, a heterogeneous memory-centric accelerator for efficient Mamba-2 inference that reformulates the matrix-form SSD computation into an algebraically equivalent streaming-recursive dataflow that avoids quadratic intermediate storage while preserving the original computation.
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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