17 citations · 18 across the 19 of their papers we have counts for
6 papers · 1 filter
A Cheap Verifier is Good Enough: LLM Post-training is Robust to Erroneous Rewards
Andreas Plesner, Curtis Northcutt, Francisco Guzmán +1
When post-training large language models on tasks with semi-verifiable rewards, there are many factors (training steps, base model size, training order, data quality, verifier accu…
An Imperfect Verifier is Good Enough: Learning with Noisy Rewards
Andreas Plesner, Francisco Guzmán, Anish Athalye
Reinforcement Learning with Verifiable Rewards (RLVR) is widely used for post-training Large Language Models, but practical verifiers can make errors. We study how the rate and str…
BitLogic: Training Framework for Gradient-Based FPGA-Native Neural Networks
Simon Bührer, Andreas Plesner, Aczel Till +1
Gradient-based LUT- and logic-gate-based neural networks (LUTNet, LogicNets, DiffLogic, PolyLUT, NeuraLUT, WARP-LUT, DWN, LILogicNet, LightLUT) replace multiply-accumulate arithmet…
From MNIST to ImageNet: Understanding the Scalability Boundaries of Differentiable Logic Gate Networks
Sven Brändle, Till Aczel, Andreas Plesner +1
Differentiable Logic Gate Networks (DLGNs) are a very fast and energy-efficient alternative to conventional feed-forward networks. With learnable combinations of logical gates, DLG…
Light Differentiable Logic Gate Networks
Lukas Rüttgers, Till Aczel, Andreas Plesner +1
Differentiable logic gate networks (DLGNs) exhibit extraordinary efficiency at inference while sustaining competitive accuracy. But vanishing gradients, discretization errors, and…
Recurrent Deep Differentiable Logic Gate Networks
Simon Bührer, Andreas Plesner, Till Aczel +1
While differentiable logic gates have shown promise in feedforward networks, their application to sequential modeling remains unexplored. This paper presents the first implementati…