Archived
2026-09-29Improving Test-Time Scaling with Adaptive Looped Transformers
Yichen You, Tianyu Fu, Aosong Feng, Xingtai Lv, Xuefei Ning, Ning Ding, et al.
arXiv:2609.35748v1Today’s pick is Improving Test-Time Scaling with Adaptive Looped Transformers. The problem it tackles is simple to state but hard to solve: as language models think longer at inference time, can we spend extra compute only where it actually improves the answer? Standard looped transformers reuse the same layers repeatedly, which can boost reasoning, but they waste effort on tokens that don’t need more refinement. This paper introduces TaH2, an adaptive looping method that learns to decide which tokens deserve another pass. It trains a backbone model together with an iteration decider using lookahead supervision, so the model can predict whether more computation will help before spending it. The result is better accuracy per unit of test-time compute, especially on hard reasoning benchmarks like AIME. Why it matters is that test-time scaling is becoming a major lever for improving model performance without retraining from scratch, and this work shows that smarter allocation of compute can beat brute-force repetition.
Also spotted that day
FurE: Efficient Instance-Specific 3D Fur Reconstruction without Animal-Fur DatasetsTelescopic Language ModelsPDMD: Projected Distribution Matching Distillation for Video Diffusion ModelsLearning Native Reflection in Unified Models with Interleaved Reinforcement LearningRetrieving Biblical Intertextual References in Karen Blixen's Seven Gothic TalesUnifying Distributional Training for One-Step Visual GenerationScaling Long-Form Story Generation via Narrative State TrackingTokenCast: Forecasting Token Consumption During LLM Agent ExecutionStatistical Learning of Contractive Dynamical Representations for Composite Adaptive ControlNeural Harmonic Measure OperatorHow to Loop MoE: Flatten the Experts, Untie the AttentionKV-streams for Efficient Compaction in Agentic Reinforcement LearningTowards Communication-Efficient Social Intelligence in Language AgentsCopy the Same, Distill the Difference: Initializing Linear Vision Transformers
Previous daily papers
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