通过中间层循环增强模型推理能力，无需重训即可提升数学推理表现
T^2MLR: Transformer with Temporal Middle-Layer Recurrence
- 将前一词的中间层特征缓存并注入当前词的早期层，实现跨步推理状态保留
- 仅在20%网络中引入循环就超越全层循环，1.7B模型微调后数学推理显著提升
- 无需从头预训练，可快速集成到现有模型，适合实际部署场景
Transformer推理受限于自回归解码过程，中间计算状态在词空间中反复压缩，难以持久。我们提出基于潜在推理架构的时序中间层循环变压器（T²MLR），将前一词的中间层表示直接融合到当前词位置的较早层中，使抽象中间计算能跨解码步骤持续存在，且推理开销极小。在自然语言预训练和多跳推理微调任务中，T²MLR始终优于参数与数据量匹配的Transformer基线。尤其值得注意的是，仅对局部中间层块（低至20%网络）引入循环，便常优于全层循环。更重要的是，T²MLR无需从头预训练：将循环路径嵌入已有的1.7B Transformer模型，经简短微调即可显著提升数学推理能力，大幅降低实际应用门槛。结果表明，有效的潜空间推理无需像以往工作那样遍历所有层进行循环，而是可通过有针对性的中间层循环更高效地涌现。
原文摘要 · Abstract (English)
Transformer reasoning is limited by autoregressive decoding, which repeat edly compresses rich hidden computation through token space and makes it difficult for intermediate reasoning states to persist across time. We in troduce Transformers with Temporal Middle-Layer Recurrence (T2MLR), a transformers-based latent reasoning architecture that fuses a cached middle layer representation from the previous token directly into an earlier layer of the current token position, enabling abstract intermediate computation to persist across decoding steps with little inference overhead. Across natural-language pretraining and multi-hop reasoning finetuning, T2MLR consistently outperforms data- and parameter-matched Transformer base lines. Moreover, applying recurrence to only a localized middle-layer block (as little as 20% of the network) often outperforms full-layer recurrence. Im portantly, T2MLR does not require pretraining from scratch: retrofitting the recurrent pathway into an existing pretrained 1.7B Transformer and briefly finetuning substantially improves math reasoning, lowering the barrier to practical adoption. These results suggest that effective latent reasoning in Transformers does not require looping over all layers as in previous works, but can instead emerge more strongly from targeted middle-layer recurrence.
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