Unverified50% confidenceFactExact time
Flow Matching with Correction (FMC) is the training objective introduced by UniWALM, which teaches the model during training to recover from perturbed motion trajectories and adds no extra computational cost at inference time.
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50%
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Long-term
Relevance
10/21/2026
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UnverifiedCompared to score-function-based diffusion models, the flow matching training objective is more concise, and the trajectory can be designed as a rectified flow, enabling high-quality generation in fewer inference steps.69% similarUnverifiedClassifier-Free Guidance (CFG) is one of the most mainstream guidance techniques in diffusion models and flow matching models, discarding conditions with a certain probability during training and, during sampling, subtracting the unconditional prediction from the conditional one via a formula and amplifying the guidance strength.69% similarVerifiedFlow matching is a generative model training method that directly learns a deterministic ODE path from the noise distribution to the data distribution, defines a linear interpolation path xt=(1-t)x₀+tx₁, and uses a neural network to learn to predict the velocity vector at every point along that path.68% similarUnverifiedDiffusion models and flow matching absorb the sampling cost once during training by training a neural network, requiring only a forward pass at inference time.63% similarUnverifiedThe trend of integrating inference and training refers to training and inference sharing model definitions, parallel strategies and scheduling infrastructure to reduce duplicated construction of two systems; capabilities accumulated by vLLM on the inference side, such as PagedAttention and continuous batching, are gradually extending into the post-training stage.63% similar
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