Do Not Regenerate What is in Your Context: Instant Context Reuse via a Pointer Head

Large language models increasingly process long-context inputs containing content that may reappear in their outputs, making redundant token-by-token generation a source of decoding overhead. Existing efficient decoding methods, including multi-token prediction and speculative decoding, reduce sequential computation through parallel prediction or draft verification but do not explicitly learn to locate and reuse such content. We propose the Pointer-Generator Large Language Model (PG-LLM), which turns context reuse into a model-native generation action. PG-LLM emits a learned control token when reuse is appropriate and uses an attention-based pointer mechanism to predict the start and end positions of a relevant source span. The selected span is inserted into the output before autoregressive generation resumes. We train PG-LLM at 4B and 30B scales. Across three code editing benchmarks, PG-LLM 30B uses $0.71\times$ as many autoregressively decoded tokens as its base model on average and improves editing accuracy by 1.64 percentage points, while preserving comparable overall performance across three code generation benchmarks. With a vLLM serving implementation, PG-LLM improves request throughput over the corresponding base model by $1.12$–$1.28\times$ at 4B and $1.13$–$1.26\times$ at 30B. These results demonstrate that model-native context localization and reuse can reduce autoregressive decoding and deliver practical serving speedups while maintaining comparable overall code generation performance.