这篇论文发现了LLM长上下文推理时爱复制原文的毛病，用GEAR奖励方法逼模型关注关键证据，效果立竿见影，适合做RL调优的参考。
研究指出长上下文LLM存在重复复制问题，即模型将输入文本大量复制到推理轨迹中。通过将提示划分为关键证据和干扰上下文，发现根本原因是模型未充分关注关键证据。提出GEAR（Grounding Evidence-Aware Reward）奖励塑造方法，包含关键证据重叠奖励和干扰惩罚。在多个模型规模和基准测试中，GEAR相比基于准确率的强化学习平均提升最高+4.6点，且在更长上下文中效果更大。结果表明准确接地于相关证据是长上下文推理中不可或缺的能力。
Copy Less, Ground More: Overcoming Repetitive Copying in Long-Context Reasoning via Evidence-Aware Reinforcement Learning
Large language models that generate step-by-step reasoning traces have achieved strong performance on complex tasks, and extending them to long-context settings has emerged as an important frontier. However, we identify a critical failure mode in this regime: \emph{repetitive copying}, where models extensively copy text from the input into their reasoning traces rather than productively solving the problem. We show that this behavior is pervasive across frontier long-context LLMs and intensifies with context length. By separating each prompt into task-relevant key evidence and irrelevant distractor context, we further show that the root cause is insufficient grounding: models copy from the prompt indiscriminately, and those that fail to focus on key evidence are far more likely to answer incorrectly. Motivated by this diagnosis, we propose GEAR (Grounding Evidence-Aware Reward), a reward shaping method that augments the accuracy signal with a grounding reward for overlap with key evidence and a distractor penalty for overlap with irrelevant context. To enable GEAR on natural-language data, we develop an automated pipeline that constructs evidence-annotated training data from arbitrary documents. We validate GEAR across multiple model scales and benchmarks, showing consistent improvements of up to +4.6 average points over standard RL with accuracy-based rewards, with larger gains at longer contexts, while also reducing repetitive copying and thinking length. Our findings suggest that, even as long-context evaluation shifts from simple retrieval toward complex reasoning, accurate grounding in relevant evidence remains an indispensable capability with substantial room for improvement.