LTA-thinker: Latent Thought-Augmented Training Framework for Large Language Models on Complex Reasoning
Trains a small transformer module to produce continuous thought vectors for a frozen language model. Aligns those vectors with the question and important solution steps; the language model then generates a written explanation and answer.
First public: · Added to this list: November 9, 2026
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Continuous-state reasoning — Use vectors—lists of numbers—to carry intermediate reasoning from one step to the next, instead of selecting a text token at every step. Some methods use the model's hidden states; others combine several candidate token representations.