1 citations · 1 across the 11 of their papers we have counts for
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Can AI Weather Models Predict Beyond Two Weeks? A Quantitative Benchmark and Analysis of Long Rollouts
Fanny Lehmann, Firat Ozdemir, Yun Cheng +4
While AI weather models excel at short-to-medium range forecasts (up to 15 days), they frequently suffer from ill-defined "instabilities" when rolled out over longer horizons. This…
SFT-then-RL Outperforms Mixed-Policy Methods for LLM Reasoning
Alexis Limozin, Eduard Durech, Torsten Hoefler +2
Recent mixed-policy optimization methods for LLM reasoning that interleave or blend supervised and reinforcement learning signals report improvements over the standard SFT-then-RL…
Beyond Outliers: A Study of Optimizers Under Quantization
Georgios Vlassis, Saleh Ashkboos, Alexandra Volkova +2
As new optimizers gain traction and model quantization becomes standard for efficient deployment, a key question arises: how does the choice of optimizer affect model performance i…
Bridging the Gap Between Promise and Performance for Microscaling FP4 Quantization
Vage Egiazarian, Roberto L. Castro, Denis Kuznedelev +8
The recent hardware-accelerated microscaling 4-bit floating-point formats such as MXFP4 and NVFP4, supported on NVIDIA and AMD GPUs, promise to revolutionize large language model (…
HALO: Hadamard-Assisted Lower-Precision Optimization for LLMs
Saleh Ashkboos, Mahdi Nikdan, Soroush Tabesh +3
Quantized training of Large Language Models (LLMs) remains an open challenge, as maintaining accuracy while performing all matrix multiplications in low precision has proven diffic…
EfQAT: An Efficient Framework for Quantization-Aware Training
Saleh Ashkboos, Bram Verhoef, Torsten Hoefler +2
Quantization-aware training (QAT) schemes have been shown to achieve near-full precision accuracy. They accomplish this by training a quantized model for multiple epochs. This is c…
MARLIN: Mixed-Precision Auto-Regressive Parallel Inference on Large Language Models
Elias Frantar, Roberto L. Castro, Jiale Chen +2
As inference on Large Language Models (LLMs) emerges as an important workload in machine learning applications, weight quantization has become a standard technique for efficient GP…