用流匹配预测任务损失，多智能体协调算力分配，提升资源利用率。
MARA: Flow-Matching-Guided Multi-Agent Resource Allocation for Computational Resource Efficient Learning
- 基于条件流匹配预测任务未来损失轨迹
- 在调度负载下平均完成63.46%任务，比基线高8.54个百分点
- 适合动态并发训练场景，尤其对算力受限系统有实用价值
在多个学习任务同时进行且每项任务需在截止前达到目标损失但训练需求未知的情况下，如何分配有限计算资源极具挑战。现有方法虽结合在线损失预测与自适应资源分配，但通常将计算视为可连续分割的吞吐量。本文研究实际场景：任务分时到达，计算资源由离散节点提供，引入需求不确定性和序列决策约束。提出MARA框架，利用条件流匹配预测未来损失轨迹，并通过合作式多智能体自回归策略协调计算节点。采用基于势函数的进度奖励，在保持未折扣任务完成目标的同时提供中间训练反馈。在分布内、强化学习及视觉工作负载上，流匹配相比加权最小二乘显著降低剩余资源预测误差。在调度负载下，MARA平均完成63.46%的任务，较强基线LARA高出8.54个百分点，且在未见过的更重负载下仍表现更优。
原文摘要 · Abstract (English)
Allocating limited computation among concurrent learning tasks is difficult when each task must reach a target loss before a deadline but its required training effort is unknown. Existing approaches combine online loss prediction with adaptive resource allocation, yet commonly treat computation as continuously divisible throughput. We instead study a practical setting in which tasks arrive over time and computation is provided by discrete nodes. This setting introduces both uncertain demand and constrained sequential decisions. We propose MARA, which predicts future loss trajectories with conditional flow matching and coordinates compute nodes through a cooperative multi-agent autoregressive policy. A potential-based progress reward supplies intermediate training feedback while preserving the undiscounted task-completion objective. Across in-distribution, reinforcement-learning, and vision workloads, flow matching reduces remaining-resource prediction error relative to weighted least squares. At the scheduler's training load, MARA completes 63.46% of tasks on average, 8.54 percentage points above strong baseline Learning with Adaptive Resource Allocation (LARA), and remains ahead under unseen heavier workloads.
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