7 papers
Quantized Reasoning Models Think They Need to Think Longer, but They Do Not
Sanae Lotfi, Polina Kirichenko, Steven Li +1
Post-training quantization (PTQ) is widely used to deploy large language models efficiently, but its effect on reasoning models is not well understood. Across math, coding, and sci…
JacQuant: STE-Free Quantization-Aware Training via Learned Jacobian Surrogates
Kai Yi, Vignesh Vivekraja, Harshit Khaitan +1
Quantization-aware training (QAT) is widely deployed but typically relies on the Straight-Through Estimator (STE), which passes gradients through non-differentiable quantizers by f…
WinQ: Accelerating Quantization-Aware Training of Language Models Around Saddle Points
Dongyue Li, Zechun Liu, Kai Yi +6
Quantization-aware training (QAT) is widely adopted to quantize language models by training full-precision weights using gradients from the quantized model. The main bottleneck is…
Rethinking Model Efficiency: Multi-Agent Inference with Large Models
Sixun Dong, Juhua Hu, Steven Li +2
Most vision-language models (VLMs) apply a large language model (LLM) as the decoder, where the response tokens are generated sequentially through autoregression. Therefore, the nu…
CLAA: Cross-Layer Attention Aggregation for Accelerating LLM Prefill
Bradley McDanel, Steven Li, Harshit Khaitan
The prefill stage in long-context LLM inference remains a computational bottleneck. Recent token-ranking heuristics accelerate inference by selectively processing a subset of seman…
MoE-Spec: Expert Budgeting for Efficient Speculative Decoding
Bradley McDanel, Steven Li, Sruthikesh Surineni +1
Speculative decoding accelerates Large Language Model (LLM) inference by verifying multiple drafted tokens in parallel. However, for Mixture-of-Experts (MoE) models, this paralleli…