Copy Less, Ground More: Overcoming Repetitive Copying in Long-Context Reasoning via Evidence-Aware Reinforcement Learning
Researchers identify repetitive copying issue in large language models, propose evidence-aware reinforcement learning solution. This issue affects models' ability to reason in long-context settings.
- Repetitive copying is a critical issue in long-context large language models
- Evidence-aware reinforcement learning can help overcome this issue
- The proposed solution improves models' ability to reason and generate accurate traces
Large language models have shown impressive performance in complex tasks, but struggle with repetitive copying in long-context settings. This behavior involves models copying input text into their reasoning traces instead of solving problems productively.
The study reveals that this issue is widespread across state-of-the-art long-context LLMs and worsens with longer context lengths. To address this, researchers propose a novel approach called evidence-aware reinforcement learning, which separates task-relevant key evidence from irrelevant distractors.
This solution enables models to focus on essential information and generate more accurate reasoning traces. The findings have significant implications for the development of more efficient and effective AI models.
The study's results demonstrate the importance of addressing repetitive copying in long-context LLMs, paving the way for further research in this area.
Helps improve AI model efficiency and accuracy
Contributes to the development of more effective AI systems
- long-context LLMs
- Large language models designed to process and reason about long sequences of text
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