弥合潜在推理与显式推理之间的差距：循环Transformer
Bridging the Gap Between Latent and Explicit Reasoning with Looped Transformers
AI总结：
提出LOTUS方法，利用循环Transformer在潜在空间进行多步推理，通过并行监督潜在块上的CoT令牌，首次在3B规模弥合与显式CoT的差距，并将推理延迟降低2.5-6.9倍。
摘要：
语言模型通常通过显式思维链（CoT）进行推理，逐令牌生成中间步骤。潜在CoT提供了一种替代方案：它在模型的隐藏状态中执行多步推理，用连续表示替换解码令牌，以提高效率。然而，现有的潜在CoT方法在超过1B参数时表现不如显式CoT，且差距随规模扩大而增大。循环或递归深度Transformer通过重用权重增加计算深度而不增加参数，自然适合潜在推理。因此，我们询问循环Transformer是否能弥合这一差距。我们用一个简单的配方给出了肯定答案：一个循环填充Transformer，对K个潜在块并行处理R次迭代，在每个潜在位置使用黄金CoT步骤令牌的交叉熵损失，类似于显式CoT监督。我们将其实例化为LOTUS（在潜在上进行并行监督的循环Transformer）。据我们所知，LOTUS是第一个在3B规模弥合与显式CoT差距的潜在CoT方法，同时将推理阶段延迟从紧凑数学表达式到自然语言减少了2.5-6.9倍。通过基础LM头投影LOTUS循环后的潜在表示，可以恢复黄金推理步骤，甚至浮现出替代的有效中间步骤，证明其潜在空间是可解释且与CoT对齐的。消融实验证实，循环主干和对黄金CoT令牌的并行监督都是必不可少的。
英文摘要：
Language models typically reason via explicit chain-of-thought (CoT), generating intermediate steps token-by-token. Latent CoT offers an alternative: it performs multi-step reasoning in the model's hidden states, replacing decoded tokens with continuous representations for greater efficiency. However, existing latent CoT methods underperform explicit CoT beyond 1B parameters, and the gap widens with scale. Looped, or recurrent-depth, Transformers, which reuse their weights to increase computation depth without adding parameters, are a natural fit for latent reasoning. We therefore ask whether looped Transformers can bridge this gap. We answer affirmatively with a simple recipe: a looped padded Transformer that processes K latent blocks in parallel for R iterations, with a cross-entropy loss on each latent position's gold CoT-step token, similar to explicit CoT supervision. We instantiate it as LOTUS (Looped Transformers with parallel supervision on latents). LOTUS is, to our knowledge, the first latent-CoT method to bridge the gap to explicit CoT at the 3B scale, while cutting thought-phase latency by 2.5x-6.9x from compact math expressions to natural language. Projecting LOTUS's post-loop latents through the base LM head recovers the gold reasoning steps and even surfaces alternative valid intermediate steps, evidence that its latent space is interpretable and CoT-aligned. Ablations confirm that both the looped backbone and the parallel supervision on gold CoT tokens are essential. Code is available at https://github.com/yingfan-bot/lotus.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
