arXiv:2609. 01952v1 Announce Type: cross Abstract: Decentralized, serverless learning increasingly connects devices running different architectures, where the standard tool, decentralized SGD, is undefined as models with different parameter counts cannot be averaged.
arXiv:2610.28545v1 Announce Type: new Abstract: Decentralised federated learning (DFL) trains personalised models on devices that average with their neighbours rather than with a central server. Real...
Decentralised federated learning (DFL) trains personalised models on devices that average with their neighbours rather than with a central server. Real devices, however, are often offline for hours or...
arXiv:2608. 06946v1 Announce Type: cross Abstract: In gossip learning, a network of nodes trains a shared model collaboratively, without a central coordinator, by repeatedly exchanging parts of their local models.
arXiv:2608. 15215v1 Announce Type: cross Abstract: Token-level knowledge distillation (KD) matches two conditional distributions per position, yet the standard objectives compare them pointwise: a Kullback-Leibler gradient is blind to which wrong token receives probability mass.
arXiv:2608.29472v1 Announce Type: cross Abstract: Knowledge distillation trains a small student model to reproduce the outputs of a large teacher model, and its progress is typically monitored throug...
arXiv:2610.07655v1 Announce Type: cross Abstract: Discrete diffusion models have emerged as a practically successful framework for generative modeling on discrete product spaces, yet their statistica...
arXiv:2607. 04113v1 Announce Type: new Abstract: Diffusion and flow-matching samplers integrate a learned probability-flow ODE from a large noise scale down to a small terminal floor $\sigma_{\min}$, at which the score is stiff and the flow develops a boundary layer.
arXiv:2606. 02646v1 Announce Type: cross Abstract: Inference-time multi-agent LLM scaling lacks a shared unit: counting nominal agents conflates cost with independent evidence.
arXiv:2609.37745v1 Announce Type: new Abstract: Neural scaling, in which loss falls as a power law with training, is central to large language models, and one recent proposal is that a $1/3$ exponent...
arXiv:2606. 30627v1 Announce Type: cross Abstract: Conservative offline training is widely advocated as a safe foundation for subsequent online adaptation: if a policy stays close to well-supported behaviour, the argument goes, it is less likely to exploit imperfections in a learned reward model.
The paper investigates the statistical properties of Reverse‑Kullback‑Leibler (KL) policy distillation, focusing on how different feedback settings affect sample complexity. It shows that when the teacher reveals its full action distribution, online distillation achieves minimax rates, whereas revealing only the probability of the student‑generated action requires substantially more data. In contrast, offline distillation attains the same minimax rate regardless of whether the teacher’s full distribution or only the probability of the teacher‑generated action is revealed, because teacher‑generated actions expose the teacher’s preferences directly.
arXiv:2609.00446v1 Announce Type: new Abstract: Conventional federated learning (FL) relies on parameter averaging, which forces clients to be doubly homogeneous: it demands an identical architecture...