aiqbench analysis
This research introduces Soft Latent Thinking, a technique that bypasses the computationally expensive vocabulary head during reasoning to allow continuous autoregressive generation in embedding space. By maintaining thought processes as continuous vectors rather than discrete tokens, the method improves pass@k metrics while reducing per-step compute costs on standard LLM architectures. It represents a significant architectural shift toward more efficient reasoning mechanisms within large language models.
A Model with No Head and Many Thoughts
arXiv:2608.31069v1 Announce Type: cross Abstract: 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.
Abstract: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.
Machine Learning (cs.LG); Computation and Language (cs.CL)
arXiv:2608.31069 [cs.LG]
(or arXiv:2608.31069v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2608.31069 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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