7 papers
Flash PD-SSM: Memory-Optimized Structured Sparse State-Space Models
Aleksandar TerziÄ, Francesco Carzaniga, Nicolas Menet +4
State-space models (SSMs) face a fundamental trade-off between efficiency and expressivity that is mainly dictated by the structure of the model's transition matrix. Unstructured t…
Locally Coherent Parallel Decoding in Diffusion Language Models
Michael Hersche, Nicolas Menet, Ronan Tanios +1
Diffusion language models (DLMs) have emerged as a promising alternative to autoregressive (AR) models, offering sub-linear generation latency and bidirectional capabilities that a…
POETS: Uncertainty-Aware LLM Optimization via Compute-Efficient Policy Ensembles
Nicolas Menet, Andreas Krause, Abbas Rahimi
Balancing exploration and exploitation is a core challenge in sequential decision-making and black-box optimization. We introduce POETS (licy nsembles for…
A Theoretical Analysis of Test-Driven Code Generation
Nicolas Menet, Michael Hersche, Andreas Krause +1
Code assistants are increasingly utilized in test-driven software development, yet the theoretical mechanisms behind their environment-interaction strategies remain underexplored.…
Thompson Sampling via Fine-Tuning of LLMs
Nicolas Menet, Aleksandar TerziÄ, Michael Hersche +2
Bayesian optimization in large unstructured discrete spaces is often hindered by the computational cost of maximizing acquisition functions due to the absence of gradients. We prop…
Structured Sparse Transition Matrices to Enable State Tracking in State-Space Models
Aleksandar TerziÄ, Nicolas Menet, Michael Hersche +2
Modern state-space models (SSMs) often utilize transition matrices which enable efficient computation but pose restrictions on the model's expressivity, as measured in terms of the…