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This paper introduces Perception-RFT, a novel training framework for multimodal document question answering that utilizes Group Relative Policy Optimization (GRPO) to directly align visual features with structured outputs, circumventing the need for reasoning tokens. The study reveals that reasoning-enabled models tend to suppress their reasoning traces during training, leading to a significant reduction in inference token length by over 60% at the 4B parameter scale, while also demonstrating that reasoning-centric approaches underperform compared to perception-based methods. Additionally, the authors identify a new phenomenon termed Grounding Divergence, highlighting a trade-off between semantic robustness and geometric precision in multimodal settings, which has implications for training efficiency and data requirements.
Bypassing reasoning tokens can cut inference costs by over 60% while maintaining precision in multimodal document QA.
Efficient multimodal document question answering with explicit visual grounding, locating the precise document region that supports each answer remains an open challenge. Current approaches bifurcate into Supervised Fine-Tuning (SFT), which requires large annotated datasets and reaches optimization plateaus, and reasoning-centric Reinforcement Learning (RL), which depends on verbose intermediate traces that inflate inference token cost without clear benefit. We introduce Perception-RFT, a training framework that applies Group Relative Policy Optimization (GRPO) to multimodal document QA, bypassing intermediate reasoning tokens to directly align visual features with structured grounding outputs. To rigorously evaluate the necessity of reasoning, we construct a reasoning variant under identical reward settings. We find that reasoning-enabled models suppress their reasoning traces during training, converging to direct perception-based policies at the 4B parameter scale, reducing per-query inference token length by more than 60%, while reasoning-enabled RL underperforms perception-only training. Through a fine-grained analysis of Qwen3-VL-4B optimization dynamics, we confirm that SFT saturation and cold-start RL instability established in text-domain post-training extend to multimodal, and identify a previously uncharacterized Grounding Divergence: a selective trade-off between semantic robustness and geometric precision on two out of distribution (OOD) benchmarks (4,828 samples) under joint RL optimization. We further show that an early SFT$\rightarrow$RL transition achieves comparable precision with 65% less training data.
