arXiv AI July 7, 2026 By Maxwell Twelftree, David Lemphers, An-chi He, Yue Yang

Not Every Sync Is Safe: Calibrated DiLoCo Scheduling for Shared AI Infrastructure

arXiv:2607. 02544v1 Announce Type: cross Abstract: DiLoCo-style training reduces communication by letting learner islands train locally before occasional outer synchronization, making it attractive for fragmented industrial AI fleets where training shares hardware with latency-sensitive serving.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning

Aug 11

When Does Trace-Driven Evaluation Mislead MoE Expert Caching? Replay Semantics, Workload Contamination, and Operating Regimes

arXiv:2608. 07911v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models have outgrown accelerator memory, and offloading expert weights to host memory is now standard.

By Yu Zhang

arXiv Machine Learning

Aug 18

CFR without Unbiasedness: Deterministic Guarantees for Persistent Public-Chance Schedules

arXiv:2608. 14761v1 Announce Type: cross Abstract: At a finite public-chance cut, counterfactual regret minimization (CFR) must choose how many outcomes to evaluate before each regret update.

By Jiaxing Guo, Lei Ye

arXiv Machine Learning

Aug 26

Reproducible Evaluation of MoE Expert Caching: Replay Semantics, Workload Contamination, and Operating Regimes

arXiv:2608.07911v4 Announce Type: replace Abstract: Mixture-of-Experts (MoE) models have outgrown accelerator memory, and offloading expert weights to host memory is now standard. This makes expert c...

By Yu Zhang

arXiv AI

Jun 11

Small Experiments, Cheaper Decisions: A Case Study in Staged Promotion for Micro-Pretraining

arXiv:2606. 11387v1 Announce Type: cross Abstract: Short pretraining runs can reduce experimental cost, but they can also over-promote configurations that only look strong at tiny budgets.

By Felipe Chavarro Polania

arXiv AI

Jun 4

EvalStop: Using World Feedback to Detect and Correct Reward Overoptimization in Multi-Tenant RLHF Platforms

arXiv:2606. 04145v1 Announce Type: cross Abstract: Cloud LLM fine-tuning platforms increasingly serve RLHF workloads, where a learned reward model is optimized as a proxy for human quality.

By Guilin Zhang, Chuanyi Sun, Shahryar Sarkani, John M. Fossaceca

llms reinforcement-learning fine-tuning

arXiv AI

4d ago

When Should Agents Check External State? Budgeting Observations for Stored Intentions

arXiv:2609.37125v1 Announce Type: new Abstract: Prospective memory allows an agent to retain an intention tied to a future condition, but the stored intention does not reveal whether that condition c...

By Zhengkun Di, Bin Shi, Kai Sun, Yiming Xu, Bo Dong

agents benchmarks

One email a morning, machine-written

Related stories

When Does Trace-Driven Evaluation Mislead MoE Expert Caching? Replay Semantics, Workload Contamination, and Operating Regimes

CFR without Unbiasedness: Deterministic Guarantees for Persistent Public-Chance Schedules

Reproducible Evaluation of MoE Expert Caching: Replay Semantics, Workload Contamination, and Operating Regimes

Small Experiments, Cheaper Decisions: A Case Study in Staged Promotion for Micro-Pretraining

EvalStop: Using World Feedback to Detect and Correct Reward Overoptimization in Multi-Tenant RLHF Platforms

When Should Agents Check External State? Budgeting Observations for Stored Intentions