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cs.LG2025
Transition Matching: Scalable and Flexible Generative Modeling
Neta Shaul, Uriel Singer, Itai Gat +1
Diffusion and flow matching models have significantly advanced media generation, yet their design space is well-explored, somewhat limiting further improvements. Concurrently, auto…
cs.LG2025
Corrector Sampling in Language Models
Itai Gat, Neta Shaul, Uriel Singer +1
Autoregressive language models accumulate errors due to their fixed, irrevocable left-to-right token generation. To address this, we propose a new sampling method called Resample-P…