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…
StudentBench: AI and human tutoring yield equivalent GRE learning gains
Curtis Northcutt, Inaara Hasmani, Kevin Feng +3
Artificial intelligence offers an unprecedented opportunity to augment human capabilities, yet progress at the frontier has focused primarily on advancing model capabilities. We in…
GraphARC: A Comprehensive Benchmark for Graph-Based Abstract Reasoning
Saku Peltonen, August Bøgh Rønberg, Andreas Plesner +1
Relational reasoning lies at the heart of intelligence, but existing benchmarks are typically confined to formats such as grids or text. We introduce GraphARC, a benchmark for abst…
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…
GIC-DLC: Differentiable Logic Circuits for Hardware-Friendly Grayscale Image Compression
Till Aczel, David F. Jenny, Simon Bührer +3
Neural image codecs achieve higher compression ratios than traditional hand-crafted methods such as PNG or JPEG-XL, but often incur substantial computational overhead, limiting the…