Post-training large language models often require optimizing multiple objectives simultaneously, motivating multi-reward reinforcement learning (RL) methods that model different objectives as separate reward signals. Existing methods like Group reward-Decoupled Policy Optimization (GDPO) compute group-normalized advantages separately for each reward dimension and then aggregate them for policy updates. However, this aggregation process can cause cross-reward advantage conflict: the same rollout may have positive advantages on some reward dimensions but negative advantages on others, causing positive and negative signals to cancel each other during aggregation. To address this issue, we propose GD²PO, short for Group-Dynamic Reward-Decoupled Policy Optimization, a conflict-aware multi-reward policy optimization method. Before advantage aggregation, GD²PO uses conflict-aware criteria to identify and filter rollouts with severe reward-wise disagreement, reducing the influence of unreliable update signals. It further introduces query-level reweighting to dynamically adjust the update strength of each query according to its overall reward consensus. Experiments on tool calling and helpfulness-safety alignment show that GD²PO outperforms existing methods. Our code is available at https://github.com/Qwen-Applications/GD2PO.

Large language model (LLM)-powered multi-agent systems (MAS) demonstrate remarkable collective intelligence, with multi-agent memory serving as a pivotal mechanism for continual adaptation. However, existing multi-agent memory designs remain constrained by two fundamental bottlenecks: (i) memory homogenization caused by limited role-aware customization, and (ii) information overload induced by exposing agents to excessively fine-grained textual memories. To address these limitations, we propose LatentMem, a learnable multi-agent memory framework that customizes agent-specific experiential memories while reducing textual context. Specifically, LatentMem comprises an experience bank for lightweight storage of raw interaction trajectories and a memory composer that synthesizes compact latent memories conditioned on retrieved experience and agent-specific contexts. Further, we introduce Latent Memory Policy Optimization (LMPO), which propagates task-level optimization signals through latent memories to learn compact, high-utility latent representations. Extensive experiments across six benchmarks and four mainstream MAS frameworks show that LatentMem improves vanilla settings by up to 19.36% and consistently outperforms existing memory architectures, without requiring modifications to the agent-layer workflows.