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cs.LG2026
Rethinking Calibration for High-Sparsity LLM Pruning: From Single Sources to Capability Coverage
Hu Xu, Zhaolong Xing, Congcong Liu +5
Post-training LLM pruning relies on a small calibration set, usually chosen heuristically and judged by WikiText perplexity or by mean accuracy on general-domain commonsense benchm…
cs.LG2026
Meta-TTRL: A Metacognitive Framework for Self-Improving Test-Time Reinforcement Learning in Unified Multimodal Models
Lit Sin Tan, Junzhe Chen, Xiaolong Fu +5
Existing test-time scaling (TTS) methods for unified multimodal models (UMMs) in text-to-image (T2I) generation primarily rely on search or sampling strategies that produce only in…