← Back to all news
arXiv Machine Learning By Gihyeon Sim

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

Read the original on arXiv Machine Learning →

The paper introduces PonderTTT, a gating strategy that uses the self‑supervised reconstruction loss from a Test‑Time Training (TTT) layer to decide when to apply TTT updates during inference. The gating mechanism is training‑free, relying only on a single scalar threshold that is calibrated on unlabeled data and updated via exponential moving average to keep the desired update rate. Experiments on GPT‑2 models (124M–1.5B) for code language modeling show that this approach achieves 82–89% oracle recovery and outperforms random skip baselines, reducing loss on out‑of‑distribution languages by up to 16%.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv Machine Learning
Jul 10

Selective Left-Shift: Turning Test-Time Compute and Difficulty-based Curation into Training Data for Low-Resource Code Generation

arXiv:2607. 07748v1 Announce Type: new Abstract: Large Language Models achieve strong code generation for high resource languages like Python and Java but suffer sharp performance drops on Low-Resource Programming Languages~(LRPLs) such as Julia.

By Didula Samaraweera, Anjana Supun, Srinath Perera
llmsreinforcement-learningfine-tuningbenchmarks
More like this →
arXiv Machine Learning
Sep 14

Performance, Efficiency and Collapse -- Advantages and Challenges in Offline Post-training of Code LLMs

The paper investigates whether reinforcement‑learning post‑training of code‑generating large language models can be done entirely offline using existing datasets, avoiding costly online code generation and GPU‑CPU communication. Experiments show that a few hours of offline RL can substantially boost zero‑shot code generation performance across models from 0.5 B to 7 B parameters, though the magnitude of improvement differs by model family.

By Abhinav Anand, Sanjana Reddy Pachika, Shweta Verma, Mira Mezini
llmsreinforcement-learning
More like this →
arXiv AI
2d ago

Rethinking Pre-pretraining

The study evaluates synthetic pre‑pretraining (PPT) across four curricula and three training regimes, finding that allocating compute to PPT never outperforms pure natural‑language training in compute‑matched experiments. Even when natural language data are scarce, repeated epochs over a small natural‑language corpus beat all synthetic curricula on validation loss, and no consistent downstream accuracy advantage is observed. The authors note that PPT gains are sensitive to weight initialization and that models tend to forget PPT knowledge during subsequent natural‑language training, suggesting limited benefits for PPT as a distinct training stage.

By Alexander Stern, Tim Rockt\"aschel, Acyr Locatelli, Laura Ruis
llms
More like this →