研究人员提出Recirculation技术，让Gemma3模型困惑度降23%，准确率提21%，还不增加推理延迟。
Recirculation技术为现成基础模型提供推理时架构增强，在Gemma3系列上实现困惑度降低23%。该方法在生成过程中几乎不增加延迟，但在预填充阶段需要串行处理。自适应变体在冻结原始模型权重的同时仅需轻微调整超参数，在GSM8k基准测试上准确率提高21%。该技术通过利用模型自身特性指导架构修改，实现了训练-free的显著性能提升。
Recirculation
We describe an inference-time architectural enhancement for off-the-shelf foundation models that markedly reduces perplexity and boosts accuracy across generation and reasoning tasks. Our approach incurs essentially no additional latency during generation, though it requires serial processing in the prefill phase. Motivated by the fundamental limitation that state updates in feedforward transformers are bounded by model depth, our technique, recirculation, introduces a specific form of recurrence that allows the model to act as a dynamical system and track belief states. We distinguish this technique from chain-of-thought computation---which is better reserved for complex inferences rather than basic state tracking---as well as from popular depth-recurrence techniques (looping) and the costly training of recurrent transformers. We also propose and evaluate an adaptive variant of recirculation which requires only light tuning of hyperparameters while freezing the original model weights. Relative to the off-the-shelf baseline, adaptive recirculation achieves remarkable gains on the Gemma3 family, including a 23% reduction in perplexity on a suite of datasets, a 21% increase in accuracy on GSM8k, and reliable improvements in accuracy on other downstream tasks. Our training-free approach succeeds by leveraging the model itself to inform architectural modifications, suggesting a route to architectural evolution guided by a trained network's properties rather than forced, arbitrary design choices.