让大模型推理时用最近邻数据微调，显著降低困惑度。

We reproduce the central claims of Test-Time Training on Nearest Neighbors for Large Language Models (Hardt and Sun, 2024), which proposes adapting a language model at inference time by fine-tuning on retrieved nearest-neighbor sequences. Using pretrained RoBERTa embeddings indexed with Faiss, we retrieve 20 neighbors per test input and apply one gradient update per neighbor across GPT-2 (117M, 774M), GPT-Neo (1.3B), and R1-Distilled-Qwen2.5-1.5B. Our experiments confirm that test-time training significantly reduces perplexity and bits-per-byte metrics across diverse domains from The Pile, with the largest improvements in structured or specialized datasets such as GitHub and EuroParl. We further validate that models not pretrained on The Pile benefit more from this adaptation than models already trained on similar data, allowing smaller models to approach the performance of larger ones. Due to infrastructure limitations, we introduce a memory-efficient retrieval implementation that loads only required line offsets rather than entire files, reducing RAM requirements from over 128 GB per server to 32 GB. We also extend the original study by evaluating R1-Distilled-Qwen2.5-1.5B, showing that test-time training yields consistent gains even for modern reasoning-optimized architectures. Overall, our results support the robustness and generality of nearest-neighbor test-time training while highlighting practical considerations for reproducing large-scale retrieval-augmented adaptation.