Liang Wu

Zhengyang Tang, Xin Lai, Pengyuan Lyu et al.

Phones are becoming an important execution surface for general-purpose agents, but training open models for reliable phone use remains difficult because the environment that matters at deployment, real devices running real apps, is slow, stateful, side-effectful, and hard to reset or verify, while scalable mock environments only approximate real behavior. We present PhoneBuddy, a training recipe and open-model line for agentic phone use that combines a real-app environment with a mock-app environment, PhoneWorld, which reconstructs runnable mock apps from real GUI usage structure. PhoneBuddy first builds a shared supervised fine-tuning stage from trajectories collected in both environments, then compares real-app RL against mixed RL across both environments. Across a 150-task human evaluation on real phones spanning apps, mini-apps, and cross-app workflows, task success rate improves from 36.67\% after supervised fine-tuning to 40.67\% after real-app RL and 45.33\% after mixed RL. On AndroidWorld, the same progression rises from 60.3\% to 77.2\% to 83.2\%. These results show that mock-app training is not a replacement for real-app RL, but a complementary source of scalable, resettable, and automatically checked interaction. The gains are strongest on app and mini-app tasks, while long-horizontal cross-app workflows remain an important open challenge.

Jiamu Zhang, Liang Wu, Mayank Darbari et al.

Modern Mixture-of-Experts (MoE) models place most of their parameters in expert layers, yet only a small fraction of those experts are used for any token. The unused weights must still be stored where the GPU can reach them. On commodity GPUs the common fix is layer-level CPU offloading, which keeps memory low but streams all of a layer's experts across PCIe on every forward pass, losing much of MoE's sparsity benefit. We cast low-resource MoE serving as a working-set management problem on the GPU: routed expert weights and the key-value (KV) cache are two streams of memory demand competing for limited VRAM. We realize this in WiSP (Working-Set Paging), a routing-aware expert pager that plugs into an unmodified serving engine with byte-identical outputs. Keeping resident only the experts a workload reuses, WiSP reaches up to 1.95x the decode throughput of static offload at the same memory budget when the model does not fit. We also find that prefetching experts from predicted routing helps little in single-stream decode: the bottleneck is PCIe bandwidth, not prediction accuracy. This shifts the question from prefetching to allocation: how should VRAM be split between experts and the KV cache? We answer with MV-WSA (Marginal-Value Working-Set Allocation), which equalizes marginal latency benefit per byte subject to a KV admission floor. MV-WSA runs either as an offline configurator or as an online controller that resizes both pools while serving. In real serving the offline configurator is the only policy we test that does well on both prefill and decode; in trace-driven simulation it stays within a few percent of a per-workflow oracle while fixed splits are about 20% worse. The online controller adds a further 1.20x without changing model outputs.