[Submitted on 29 Sep 2026]
Title:Improving OCR Faithfulness via Gated and Attenuated On-Policy Distillation
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Abstract:Vision-language models may rewrite anomalous text in images into linguistically plausible expressions, compromising OCR transcription faithfulness. Sequence-level task rewards and local teacher guidance are complementary, but guidance from the same teacher may not remain equally effective as the student improves. Offline analysis shows that supervision from a fixed teacher becomes progressively less favorable as the student improves, both across training checkpoints and across response groups with different task rewards. Motivated by this observation, we introduce GAD-RL, which adaptively regulates teacher supervision during joint post-training according to the student's current task performance and local distributions. A frozen teacher conditions on reference transcriptions and student-generated prefixes. GAD-RL disables distillation for response groups containing an output with task reward at least 0.95 and continuously attenuates distillation strength as group-mean reward increases. It also weights forward KL by the student's probability of the teacher's Top-1 token, moderating local auxiliary updates when student support for that candidate is low. On Qwen3.5-2B, GAD-RL achieves 59.92% Micro Recall on CHAOS-Bench, surpassing GRPO and GRPO+OPD (fixed-weight) by 8.45 and 4.43 percentage points, respectively, while achieving an Overall score of 91.18 on OmniDocBench v1.6.
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2609.38282 [cs.AI]
(or arXiv:2609.38282v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.38282
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Baode Wang [view email] [v1] Tue, 29 Sep 2026 16:15:21 UTC (1,004 KB)
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