2.8m Gmail.txt Now

: The model is tested on subsets ranging from 200k to 2.8 million samples.

) to ensure the generated code matches the visual intent [11]. 2.8M GMAIL.txt

The paper demonstrates that MSRL significantly outperforms pure SFT models by optimizing for both textual structure and visual fidelity, effectively surpassing the performance limit reached at 2.8M SFT samples [11, 25]. MSRL Stage Max Dataset Size 2.8 million samples [11, 22] 33k curated samples [11] GPU Requirement 16 H800 GPUs [11] 24 H800 GPUs [11] Training Goal Min. Negative Log-Likelihood [22] Hybrid Text-Visual Reward [11] Outcome Performance Plateaus [22] Breaks SFT Performance Limit [11] : The model is tested on subsets ranging from 200k to 2

: Uses 22k data pairs focusing on textual accuracy ( MSRL Stage Max Dataset Size 2

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