When to Ponder: Adaptive Compute Allocation for Code Generation via Test-Time Training

Abstract:Large language models apply uniform computation to all inputs, regardless of difficulty. We propose PonderTTT, a gating strategy using the TTT layer's self-supervised reconstruction loss to selectively trigger Test-Time Training (TTT) updates. The gating decision itself is training-free--requiring no learned classifier or auxiliary networks; only a single scalar threshold is initially calibrated on unlabeled data and continuously adapted via EMA to maintain target update rates. Our experiments with GPT-2 models (124M to 1.5B) on code language modeling (The Stack v2, teacher-forced perplexity) demonstrate that this signal is inference-compatible, requiring no ground-truth labels. Our Reconstruction Gating achieves 82-89% Oracle Recovery while being fully training-free, significantly outperforming Random Skip baselines (up to 16% lower loss on OOD languages).

Machine Learning (cs.LG); Computation and Language (cs.CL)

arXiv:2601.00894 [cs.LG]

(or arXiv:2601.00894v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2601.00894 Focus to learn more arXiv-issued DOI via DataCite

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