2.8m Gmail.txt -

) to ensure the generated code matches the visual intent [11].

: Qwen2.5-VL-72B-Instruct is used as the judge model for calculating visual rewards during training [11]. 4. Experimental Results 2.8M GMAIL.txt

To break the plateau, the authors implement a two-stage Reinforcement Learning (RL) process [11]. ) to ensure the generated code matches the

: Increasing data from 2M to 2.8M results in no further performance gains, confirming the plateau [22]. Multimodal Structured Reinforcement Learning (MSRL) : 2.8M GMAIL.txt