What Makes Effective Supervision in Latent Chain-of-Thought: An Information-Theoretic Analysis
Uses information-theoretic analysis to study which supervision signals make latent chain-of-thought training effective.
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arXiv:2606.20075 · Initial submission: 2026-06-18
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Analysis and evaluation — Measure how well these methods work, investigate what their internal computation does, and identify where they fail. This includes experiments, studies of model internals, and mathematical results.
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.
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