Agentic systems solve complex tasks by coordinating multiple agents that iteratively reason, invoke tools, and exchange intermediate results. To improve robustness and solution quality, recent approaches deploy multiple agent teams running in parallel to explore diverse reasoning trajectories. However, parallel execution comes at a significant computational cost: when different teams independently reason about similar sub-problems or execute analogous steps, they repeatedly perform substantial overlapping computation. To address these limitations, in this paper, we propose Learning to Share (LTS), a learned shared-memory mechanism for parallel agentic frameworks that enables selective cross-team information reuse while controlling context growth. LTS introduces a global memory bank accessible to all teams and a lightweight controller that decides whether intermediate agent steps should be added to memory or not. The controller is trained using stepwise reinforcement learning with usage-aware credit assignment, allowing it to identify information that is globally useful across parallel executions. Experiments on the AssistantBench and GAIA benchmarks show that LTS significantly reduces overall runtime while matching or improving task performance compared to memory-free parallel baselines, demonstrating that learned memory admission is an effective strategy for improving the efficiency of parallel agentic systems. Project page: https://joefioresi718.github.io/LTS_webpage/

翻译：智能体系统通过协调多个智能体进行迭代推理、调用工具并交换中间结果来解决复杂任务。为提高系统鲁棒性与解决方案质量，近期研究部署了并行运行的多个智能体团队以探索多样化的推理轨迹。然而，并行执行会带来显著的计算成本：当不同团队独立处理相似子问题或执行类似步骤时，它们会重复执行大量重叠计算。为克服这些限制，本文提出"学会共享"（LTS）——一种用于并行智能体框架的习得式共享内存机制，该机制能在控制上下文增长的同时实现跨团队信息的选择性复用。LTS引入可供所有团队访问的全局记忆库，以及一个轻量级控制器用于决策是否将智能体中间步骤存入记忆。该控制器通过采用使用感知信用分配的逐步强化学习进行训练，使其能够识别在并行执行中具有全局价值的信息。在AssistantBench和GAIA基准测试上的实验表明，相较于无记忆并行基线方法，LTS在保持或提升任务性能的同时显著降低了总体运行时间，这证明习得式记忆准入是提升并行智能体系统效率的有效策略。项目页面：https://joefioresi718.github.io/LTS_webpage/