English(EN)Why Struggle with Continuous Latents? Interpretable Discrete Latent Reasoning via Rendered Compression
新研究探索用于大型语言模型的潜在变量推理，带来效率和可解释性提升
作者PulseAugur 编辑部·[5 个来源]·
两篇新研究论文探索了改进大型语言模型推理能力的替代方法。其中一篇论文介绍了 LoTUS（Looped Transformers with parallel supervision on latents），一种使用循环深度 Transformer 进行潜在变量推理的方法，该方法有望在规模化应用中缩小与显式思维链（CoT）方法的差距并降低延迟。另一篇论文提出了离散潜在变量推理（DLR），它将连续的潜在状态转换为离散标记，以实现更稳定和可解释的推理，在推理基准测试中实现了高达 20 倍的压缩。
AI
arXiv:2510.25741v5 Announce Type: replace Abstract: Modern LLMs are trained to "think" primarily via explicit text generation, such as chain-of-thought (CoT), which defers reasoning to post-training and under-leverages pre-training data. We present and open-source Ouro, named aft…
arXiv cs.CL
TIER_1English(EN)·Ying Fan, Anej Svete, Kangwook Lee·
arXiv:2606.31779v1 Announce Type: cross Abstract: 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…
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 greate…
arXiv:2606.29712v1 Announce Type: new Abstract: Large language models achieve high reasoning performance via explicit chain-of-thought and reinforcement learning, but require long output sequences and extended inference time. Latent reasoning reduces this cost by shifting computa…
Large language models achieve high reasoning performance via explicit chain-of-thought and reinforcement learning, but require long output sequences and extended inference time. Latent reasoning reduces this cost by shifting computation into a latent space; however, continuous la…
