Daniel Sonntag, Omar Adjali, Siting Liang, Omair Shahzad Bhatti
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End-to-end event extraction remains challenging for large language models as it requires simultaneous identification of event triggers, classification of event types, and extraction of schema-grounded argument spans. We present 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. Our reward design explicitly targets structural validity, extraction accuracy, groundedness, coverage, over-generation, and span precision. Experiments across seven benchmark datasets show that EAGER consistently outperforms prompting, supervised fine-tuning, and prior reinforcement learning baselines, achieving a substantial improvement over the strongest prior method. Results demonstrate that task-aligned verifiable rewards and contrastive advantage estimation substantially improve structured extraction.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2609.29230")
get_code_for_paper("2609.29230")
have("2609.29230")
The run record, dated, one paper per request, free:
curl https://syntology.ai/api/ran/2609.29230.json
A badge for a README (the split and the date, never a ratio):
[](https://syntology.ai/paper/2609.29230)
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