EAGER boosts generative event extraction with verifiable-reward reinforcement learning, beating prompting, supervised fine-tuning, and prior RL baselines across seven benchmarks
The work presents EAGER, a reinforcement learning framework for generative event extraction that combines fine-grained verifiable rewards with Schema-Contrastive Advantage Estimation to alleviate advantage collapse under sparse binary rewards; its reward design explicitly targets structural validity, extraction accuracy, groundedness, coverage, over-generation, and span precision, and across seven benchmark datasets it consistently outperforms prompting, supervised fine-tuning, and prior reinforcement learning baselines, achieving a substantial improvement over the strongest prior method.