Author

C. Ekbote

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#artificial intelligence Preprint Oct 2026

How to Loop Your Transformer: Loop Scaling, Specialization, and Parallel Inference

Looping in transformer models reuses weights by repeatedly applying the same transformer, or parts of it, making the model deeper without increasing its parameter count. Whether looping is useful depends on which constraint dominates: model size, training compute, or autoregressive inference efficiency. We study these...

C. Ekbote, Artidoro Pagnoni, Devendra Singh Sachan et al. · 0 citations

Jul 2026

Reward-Gated On-Policy Distillation

RG-OPD bridges sparse verifier rewards and dense teacher logits, preserving token-level supervision while filtering misleading teacher signals, and produces stronger distilled students, outperforming both vanilla reverse-KL distillation and the recent TSD-KD baseline.

Mohammad Sadegh Akhondzadeh, Vijay Lingam, Atula Tejaswi et al. · arXiv.org · 4 citations · ⚡1