Researchers Train LLMs on Disclosure Policies for Reasoning Timing

Researchers train LLMs to learn disclosure policies balancing internal reasoning and token output in autoregressive setups. The method reduces silence delays and early commitment risks.

Researchers introduced disclosure policies that train LLMs to decide when to reason internally versus output tokens publicly. In autoregressive single-stream setups, this separates state updates from visible commitments. The method learns optimal timing to balance deliberation and promptness.