CDM amortizes SMC inference for reward-tilted discrete diffusion by training a parameterized twist function on contrastive samples with closed-form kernels.
Test-time scaling of diffusion models via noise trajectory search
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NTRK is a reward-guided diffusion sampler that uses a whitening operator to bias the noise term toward high-reward outcomes, outperforming baselines with up to 20x fewer sampling steps on aesthetic tasks.
Stream-T1 is a test-time scaling framework for streaming video generation using scaled noise propagation from history, reward pruning across short and long windows, and feedback-guided memory sinking to improve temporal consistency and visual quality.
A survey of test-time scaling for multimodal foundation models that introduces a three-way taxonomy of sampling, feedback, and search approaches along with applications and benchmarks.
citing papers explorer
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Contrastive Distribution Matching for Amortized Sequential Monte Carlo in Discrete Diffusion
CDM amortizes SMC inference for reward-tilted discrete diffusion by training a parameterized twist function on contrastive samples with closed-form kernels.
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NoiseTilt: Noise-Tilted Reverse Kernels for Diffusion Reward Alignment
NTRK is a reward-guided diffusion sampler that uses a whitening operator to bias the noise term toward high-reward outcomes, outperforming baselines with up to 20x fewer sampling steps on aesthetic tasks.
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Stream-T1: Test-Time Scaling for Streaming Video Generation
Stream-T1 is a test-time scaling framework for streaming video generation using scaled noise propagation from history, reward pruning across short and long windows, and feedback-guided memory sinking to improve temporal consistency and visual quality.
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Test-Time Scaling in Multimodal Foundation Models: A Comprehensive Survey of Generation and Reasoning
A survey of test-time scaling for multimodal foundation models that introduces a three-way taxonomy of sampling, feedback, and search approaches along with applications and benchmarks.