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 models (LLMs) can generate fluent peer reviews but often miss sparse, subtle, and document-level scientific errors. We introduce PaperAudit-Bench, a benchmark for evaluating paper-level error detection and error-aware automated review. It comprises two components: (1) PaperAudit-Dataset, 220 high-quality AI/ML papers containing more than 2,200 controlled synthetic errors across eight categories; and (2) PaperAudit-Review, an evidence-aware framework that links structured error detection to review generation. Experiments across models, error categories, paper sections, and analysis depths show that sparse long-context auditing remains challenging for current LLMs. Conditioning review generation on explicit, manuscript-grounded error evidence produces more conservative and discriminative assessments that align more closely with human judgments than representative automated-review baselines. The dataset also supports post-training compact detectors through SFT and RL; Qwen3-14B achieves 55.8% F1 at Detection@1 under a fixed held-out protocol. A PubPeer transfer study and deployment in ScienceOne, a scientific foundation-model platform led by the Chinese Academy of Sciences, further illustrate practical applicability.

Code and Dataset Page: https://github.com/TU2021/PaperAudit

ScienceOne Manuscript Review: https://www.scienceone.cn/lit/#/assistant