Copy Less, Ground More: Overcoming Repetitive Copying in Long-Context Reasoning via Evidence-Aware Reinforcement Learning

Many assumed that once long-context models could reason, the old challenge of simply finding the right text was behind us. This paper shows that is wrong. Even strong reasoning models slip into a lazy habit of copying big chunks of the input into their own thinking, and the habit gets worse the longer the input is, quietly eating their token budget and dragging accuracy down. Copying itself is fine; the damage comes from copying the wrong stuff. Models that copy the few lines that actually matter tend to get the answer right, while models that copy the surrounding filler tend to get it wrong. They developed a simple reward that stops long-context language models from mindlessly copying their input, lifting accuracy by up to 4.6 points. The fix rewards the model for engaging with the small set of text that actually matters and penalizes it for copying the irrelevant filler around it. – arxiv. org/abs/2607.19345 Title: "Copy Less, Ground More: Overcoming Repetitive Copying in Long-Context Reasoning via Evidence-Aware Reinforcement Learning"