Enhancing AMR Parsing with Group Relative Policy Optimization

We investigate the capabilities of the openly available Llama 3.2 1B language model for Abstract Meaning Representation (AMR) parsing through supervised fine-tuning, further enhanced by reinforcement learning via Group Relative Policy Optimization (GRPO).Existing supervised methods for AMR parsing face limitations due to static loss functions and challenges in capturing complex semantic phenomena.To address this, our GRPO-based approach explicitly optimizes fine-grained semantic rewards, including Smatch scores, frameargument correctness, and structural validity of logical operations.Experimental results show that supervised fine-tuning alone establishes Llama as a capable English AMR parser, and subsequent GRPO fine-tuning further improves its performance.Our final model achieves higher Smatch scores, consistently respects critical low-level semantic constraints, and outperforms existing parsers on high-level semantic evaluation metrics across diverse linguistic phenomena.