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SpecCTMC is a training-free speculative sampler for reducing the sequential model-evaluation bottleneck in exact continuous-time sampling of diffusion language models.
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11/6/2026
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UnverifiedSpecMRL's spectral basis is derived from model parameters and is end-to-end learnable, rather than being a post-hoc decomposition of fixed representations.68% similarUnverifiedThe systematic process for debugging a non-converging model is to first verify on a very small data subset whether the model can overfit; if it cannot overfit even on small data, it indicates a bug in the model or training code.66% similarUnverifiedThe Model Spec is not a prompt or system instruction, but a high-level document aimed at training, evaluation and alignment processes.66% similarUnverifiedUnlike traditional speculative decoding, SpecCTMC does not require a separate draft model and instead directly reuses the main diffusion model's own predictive distributions as the proposal distribution.66% similarUnverifiedSpecCTMC uses the predictive distributions already computed in previous steps of the model to continuously propose multiple token updates and evaluates the intermediate states in parallel.63% similar
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