Towards Understanding Pause Token Fine-Tuning Dynamics: A Mode Retention Perspective
Places pause tokens at reasoning-step boundaries and masks their training loss. Studies how this affects learning new tasks while retaining earlier capabilities, including during reinforcement learning.
First public: · Added to this list: October 18, 2026
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arXiv:2609.04489 · Initial submission: 2026-09-03
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Thinking tokens — Give a model extra tokens for computation, such as pauses, filler symbols, or learned planning tokens. These tokens need not spell out the solution.
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.
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