2.8m Gmail.txt [ DIRECT » ]

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

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] 2.8M GMAIL.txt

: Uses 22k data pairs focusing on textual accuracy ( : The model is tested on subsets ranging from 200k to 2