新方法让模型在嵌入空间连续推理，不生成离散token，性能还提升了。
研究人员提出Soft Latent Thinking方法，在推理过程中用轻量投影器替代大词汇量头。在DeepSeek-Qwen-1.5B和LLaMA-3.2-3B模型上测试，该方法在所有k值下均提升pass@k指标，同时减少思维链每步计算量。该方法在所有软思维方法中达到最高pass@32，证明连续空间可有效进行推理。
A Model with No Head and Many Thoughts
Large language models decode by projecting hidden states through a large vocabulary head at every step. This operation is computationally costly and forces all reasoning to be expressed in discrete tokens. We introduce Soft Latent Thinking, a method that replaces the LM head during reasoning with a lightweight projector, enabling autoregressive rollout in embedding space where reasoning steps remain continuous rather than tokenized. Experiments on DeepSeek-Qwen-1.5B and LLaMA-3.2-3B show that Soft Latent Thinking consistently improves pass@k across all k while reducing per-step compute during chain-of-thought. Our method achieves the highest pass@32 among all soft-thinking approaches, demonstrating that effective reasoning can be carried out in continuous space without discrete token generation.