On-policy distillation (OPD) has become increasingly popular for transferring teacher capabilities to student models. In this work, we ask a critical research question: Is on-policy sampling always beneficial for distilling arbitrary teacher-student pairs? We show that a simple alternative, Semi-OPD, which distills fro...
Siyan Zhao, Yong-Gan Fu, Jin-Dong Jiang et al.· 0 citations
Despite the success of discrete diffusion language models (dLMs) for fast parallel decoding, their non-smooth, high-dimensional space hinders trajectory steering for reasoning and inference acceleration. To overcome this, we present Sigma, the first large-scale (3B/8B) continuous dLM built on steerable, low-dimensional...
Zhi-Han Yang, Wei Guo, Jean-Marie Lemercier et al.· 0 citations
TRACK: TRajectory-Aware Capacity routing via top-K selection is presented, a heterogeneous denoising strategy that switches between compatible large and small models at selected steps, reducing the average cost per denoising evaluation.
Mustafa Munir, H. Vũ, Shreyas Misra et al.· 0 citations
Zone of Proximal Policy Optimization (ZPPO), inspired by Vygotsky's zone of proximal development, is introduced, which outperforms off/on-policy distillation and GRPO, with the largest gains at the smallest scale.
Molt is a PyTorch-native training framework built to keep that cost small: a codebase compact and clean enough for a researcher to hold in their head, and for an AI coding assistant to read and reason about in its entirety, so the algorithm flow can be traced and changed end to end.
Jian Hu, Huiying Li, Hao Zhang et al.· 0 citations
RADIO1D is introduced, which compresses images into a compact, variable-length 1D token sequence using multi-teacher knowledge distillation and an autoencoder design, delivering competitive performance on diverse multimodal benchmarks with lower computational overhead and better accuracy.
Greg Heinrich, Michael Ranzinger, Collin McCarthy et al.· 0 citations
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