3 papers
cs.LG2026
OPEN-1B: A Fully Auditable Training Run
John Donaghy, Brian Wilcox, Oğuzhan Ersoy +6
Open-source language models have a reproducibility problem. Despite releasing weights, training data, and recipes, none of them are provably reproducible due to the non-associativi…
cs.LG2025
Sharing is Caring: Efficient LM Post-Training with Collective RL Experience Sharing
Jeffrey Amico, Gabriel Passamani Andrade, John Donaghy +12
Post-training language models (LMs) with reinforcement learning (RL) can enhance their complex reasoning capabilities without supervised fine-tuning, as demonstrated by DeepSeek-R1…
cs.LG2025
Verde: Verification via Refereed Delegation for Machine Learning Programs
Arasu Arun, Adam St. Arnaud, Alexey Titov +6
Machine learning programs, such as those performing inference, fine-tuning, and training of LLMs, are commonly delegated to untrusted compute providers. To provide correctness guar…