PonderLM-3: Adaptive Token-Wise Pondering with Differentiable Masking
Test-time scaling has shown that allocating more additional computation at inference can improve generation quality, motivating a natural follow-up question: where should this computation be spent? Building on this insight, we introduce PonderLM-3, a pretraining framework for token-wise adaptive pondering that learns to selectively allocate additional computation under purely self-supervised objectives, built on top of the PonderLM-2 backbone. This makes additional inference computation an allocatable per-token resource, so tokens receive more computation only when it is beneficial, rather than paying a uniform extra cost. To make this allocation learnable while maintaining train-inferenc...
| Authors | He Li, Feichen Song, Boyi Zeng et al. |
|---|---|
| Primary category | cs.CL |
| Published | 2 March 2026 |
| Last updated at source | 10 March 2026 |
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