Reasoning-free training cuts inference cost for document QA systems
Researchers propose Perception-RFT, a training method that strips intermediate reasoning steps from multimodal document QA systems to improve efficiency. By applying Group Relative Policy Optimization directly to visual grounding outputs, the approach sidesteps the token overhead of reasoning-heavy RL while avoiding the annotation burden of supervised fine-tuning. The work challenges a core assumption in recent LLM training: that explicit reasoning traces improve performance. For practitioners building document understanding systems, this signals a potential efficiency frontier where inference cost drops without sacrificing accuracy on grounded visual QA tasks.
Modelwire context
ExplainerThe paper's core claim isn't just efficiency; it's that intermediate reasoning traces may be a training artifact rather than a necessity. The authors show GRPO can optimize directly on final outputs without the token overhead of chain-of-thought supervision, suggesting the reasoning-first paradigm in recent LLM work may be overconstrained.
This directly echoes the finding from the multi-agent debate paper (July 2026) that simpler, direct approaches often outperform elaborate reasoning orchestration. Both studies challenge the assumption that more cognitive steps or more model instances yield better results. The financial distillation work from the same week also points toward a similar insight: execution-verified signals matter more than natural-language explanations. Across these three papers, a pattern emerges: practitioners are discovering that intermediate representations (reasoning traces, debate transcripts, textual rationales) add cost without proportional gains, and that direct supervision on outcomes is sufficient.
If Perception-RFT maintains accuracy parity with reasoning-based baselines on the DocVQA and InfographicVQA benchmarks when tested on held-out document types (e.g., scanned PDFs vs. digital documents), the efficiency claim holds. If performance degrades on out-of-distribution visual grounding tasks, the approach may only work for narrow, well-defined document QA and won't generalize to messier real-world scenarios.
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MentionsPerception-RFT · Group Relative Policy Optimization · GRPO
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Modelwire summarizes, we don’t republish. arXiv cs.CL originally reported this story as “Stop Thinking, Start Looking: Efficient Post-Training for Multimodal Document Question Answering via Reasoning-Free Alignment”. The full content lives on arxiv.org. If you’re a publisher and want a different summarization policy for your work, see our takedown page.