让大模型在连续空间中高效推理，提升准确率并缩短思考长度。
Latent Thought Flow: Efficient Latent Reasoning in Large Language Models
- 将推理过程建模为可变长度的连续轨迹，用采样器匹配奖励驱动的后验分布。
- 在微调和迁移学习下，准确率提升9.5%，推理长度减少27.2%。
- 适合需要高效推理的场景，如资源受限部署或复杂任务规划。
大型语言模型日益依赖中间推理，但传统的思维链（CoT）受限于语言空间瓶颈：每个思考步骤必须解码为标记，导致高推理开销。隐式推理将思考过程移入连续空间，但现有方法多为确定性或奖励最大化路径，缺乏对不同正确性与计算成本轨迹的概率分配机制。本文提出隐式思维流（LTF），将推理建模为可变长度的连续轨迹，并训练采样器以匹配由奖励诱导的答案质量与计算成本后验分布。我们使用带有随机隐状态转移的连续生成流网络实现该方法。为应对稀疏答案监督，引入熵加权子轨迹平衡目标以处理中间奖励，并采用参考先验正则化锚定探索。在微调与迁移学习设置下的实验表明，LTF优于显式思维链与隐式推理基线，在平均精度上提升9.5%，推理长度减少27.2%。
原文摘要 · Abstract (English)
Large Language Models (LLMs) increasingly rely on intermediate reasoning, yet explicit Chain-of-Thought (CoT) suffers from a linguistic space bottleneck: each thought must be decoded into tokens, causing high inference overhead. Latent reasoning moves deliberation into continuous space, but existing methods mostly learn deterministic or reward-maximizing paths, lacking a principled way to allocate probability across trajectories with different correctness and costs. We propose Latent Thought Flow (LTF), which models reasoning as variable-length continuous trajectories and trains a sampler to match a reward-induced posterior over answer quality and computation cost. We instantiate this with a continuous GFlowNet using stochastic latent transitions. To handle sparse answer supervision, we introduce an Entropy-Weighted Subtrajectory Balance objective for intermediate rewards and a reference-prior regularizer to anchor exploration. Experiments under finetuning and transfer learning settings show that LTF outperforms explicit CoT and latent reasoning baselines, improving accuracy by 9.5% while reducing reasoning length by 27.2% on average compared with strong latent reasoning baselines.
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