Event Argument Extraction (EAE) aims to identify event arguments and assign semantic roles under a predefined schema.Recent work formulates EAE with large language models as a structured conditional generation task and applies Reinforcement Learning with Verifiable Rewards (RLVR) to optimize sequence-level event structures.However, RLVR-based EAE supervision is coarse-grained, as a single reward is assigned to the whole event structure, while optimization happens at the token level.This misalignment causes the same reward to be applied to all tokens, including those not related to event roles or arguments, introducing noise into the gradient updates and weakening the signals for decisions critical to argument extraction.To mitigate this misalignment, we propose Hybrid Token Masking RLVR (HTMR), which selectively updates policy gradients on both high-entropy forking tokens and eventcritical tokens that define event structure, along with multi-perspective reasoning.Experiments across multiple benchmarks and models show that HTMR consistently outperforms full-token and high-entropy only RLVR methods.Moreover, HTMR transfers effectively as a plug-andplay approach to other tasks such as named entity recognition and relation classification.The code is publicly available for reproducibility.

Multimedia Event Extraction (MEE) aims to identify events and their arguments from documents that contain both text and images. It requires grounding event semantics across different modalities. Progress in MEE is limited by the lack of annotated training data. M2E2 is the only established benchmark, but it provides annotations only for evaluation. This makes direct supervised training impractical. Existing methods mainly rely on cross-modal alignment or inference-time prompting with Vision–Language Models (VLMs). These approaches do not explicitly learn structured event representations and often produce weak argument grounding in multimodal settings. To address these limitations, we propose RMPL, a Relation-aware Multi-task Progressive Learning framework for MEE under low-resource conditions. RMPL incorporates heterogeneous supervision from unimodal event extraction and multimedia relation extraction with stage-wise training. The model is first trained with a unified schema to learn shared event-centric representations across modalities. It is then fine-tuned for event mention identification and argument role extraction using mixed textual and visual data. Experiments on the M2E2 benchmark with multiple VLMs show consistent improvements across different modality settings.