EAGER: Enhancing Generative Event Extraction via Reinforcement Learning with Verifiable Rewards

arXiv:2609.29230 · cs.CL · Submitted 2026-09-24 · Read on arXiv

cs.CL

Submitted: 2026-09-24

Updated: 2026-09-24

Code: https://github.com/huggingface/trl

Terminology

• Reinforcement Learning — 16× in this paper · explained in 4 episodes
• Information Extraction — 12× in this paper
• Benchmark — 11× in this paper · explained in 2 episodes
• Instruction Tuning — 11× in this paper · explained in 2 episodes
• Adam — 9× in this paper · explained in 2 episodes
• Fine-Tuning — 9× in this paper · explained in 5 episodes
• Verifiable Rewards — 9× in this paper · explained in 2 episodes
• Accuracy — 6× in this paper · explained in 1 episode
• Generalization — 6× in this paper · explained in 13 episodes
• Ablation — 5× in this paper · explained in 1 episode
• Alignment — 5× in this paper · explained in 1 episode
• GRPO — 4× in this paper · explained in 6 episodes
• Hallucination — 4× in this paper · explained in 3 episodes
• Ablation study — 3× in this paper · explained in 1 episode
• Trigger Identification — 3× in this paper
• Adaptation — 2× in this paper · explained in 1 episode
• Argument Identification — 2× in this paper
• Entropy Collapse — 2× in this paper · explained in 2 episodes
• F1 Score — 2× in this paper · explained in 3 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Grounding — 2× in this paper · explained in 3 episodes
• KL Regularization — 2× in this paper · explained in 2 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Reward Function — 2× in this paper · explained in 2 episodes
• Adaptability — 1× in this paper · explained in 1 episode
• Advantage Collapse Rate — 1× in this paper
• Argument Classification — 1× in this paper
• Catastrophic Forgetting — 1× in this paper · explained in 5 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Direct Preference Optimization (DPO) — 1× in this paper · explained in 9 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Homogeneity — 1× in this paper · explained in 2 episodes
• Parameter-Efficient Fine-Tuning — 1× in this paper · explained in 2 episodes
• Prompt Engineering — 1× in this paper · explained in 4 episodes
• Reward Hacking — 1× in this paper · explained in 8 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Schema-Contrastive Advantage Estimation — 1× in this paper
• Transferability — 1× in this paper · explained in 4 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Trigger Classification — 1× in this paper

Sources

• SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training
• EventRL: Enhancing Event Extraction with Outcome Supervision for Large Language Models
• Exploring the Feasibility of ChatGPT for Event Extraction
• The Llama 3 Herd of Models
• DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
• Does Math Reasoning Improve General LLM Capabilities? Understanding Transferability of LLM Reasoning
• GPT-4o System Card
• GDPO: Group reward-Decoupled Normalization Policy Optimization for Multi-reward RL Optimization
• OpenAI o1 System Card
• PIVOINE: Instruction Tuning for Open-world Information Extraction
• Cross-media Structured Common Space for Multimedia Event Extraction
• Code Llama: Open Foundation Models for Code
• Proximal Policy Optimization Algorithms
• OpenAI GPT-5 System Card
• InstructUIE: Multi-task Instruction Tuning for Unified Information Extraction
• Scaf-GRPO: Scaffolded Group Relative Policy Optimization for Enhancing LLM Reasoning

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