新闻
Copy Less, Ground More: Overcoming Repetitive Copying in Long-Context Reasoning via Evidence-Aware Reinforcement Learning
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- Researchers identified repetitive copying in long-context LLMs and developed GEAR, a reinforcement learning method that rewards grounding in relevant evidence while penalizing distractor text.
- 为何重要
- Matters for engineers building or fine-tuning large language models for long-context reasoning tasks where accuracy and efficiency are critical.
- 注意
- The method requires automated evidence annotation of training data, and improvements plateau at longer contexts; real-world applicability depends on annotation quality.
- llm
- language model
- reasoning
- prompt
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