1 citations · 1 across the 8 of their papers we have counts for
11 papers
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…
The Role of Feedback Alignment in Self-Distillation
Semih Kara, Oğuzhan Ersoy
Conditioning a language model on additional context, such as feedback on a previous attempt, typically improves its response. Self-distillation trains the model to retain this impr…
IR3DE: A Linear Router for Large Language Models
Eros Fanì, Oğuzhan Ersoy
Foundational Large Language Models (LLMs) demonstrate proficiency on a wide range of general tasks, and achieve remarkable results on various specialized tasks via domain-expert LL…
F-TIS: Harnessing Diverse Models in Collaborative GRPO
Nikolay Blagoev, Oğuzhan Ersoy, Wendelin Boehmer +1
Reinforcement learning methods such as GRPO have seen great popularity in LLM post-training. In GRPO, models produce completions to a set of prompts, which are rewarded, and the po…
Backdoor Attacks on Decentralised Post-Training
Oğuzhan Ersoy, Nikolay Blagoev, Jona te Lintelo +3
Decentralised post-training of large language models utilises data and pipeline parallelism techniques to split the data and the model. Unfortunately, decentralised post-training c…
Hail to the Thief: Exploring Attacks and Defenses in Decentralised GRPO
Nikolay Blagoev, Oğuzhan Ersoy, Lydia Yiyu Chen
Group Relative Policy Optimization (GRPO) has demonstrated wide adoption in the post-training of Large Language Models (LLMs). In GRPO, prompts are answered by the model and prefer…
All is Not Lost: LLM Recovery without Checkpoints
Nikolay Blagoev, Oğuzhan Ersoy, Lydia Yiyu Chen
Training LLMs on decentralized nodes or on-spot instances, lowers the training cost and enables model democratization. The inevitable challenge here is the transient churns of node…
NoLoCo: No-all-reduce Low Communication Training Method for Large Models
Jari Kolehmainen, Nikolay Blagoev, Semih Kara +3
Training large language models is generally done on clusters containing thousands of accelerators, communicating over a high-bandwidth interconnect. Scaling up these clusters is ex…
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…
HDEE: Heterogeneous Domain Expert Ensemble
Oğuzhan Ersoy, Jari Kolehmainen, Gabriel Passamani Andrade
Training dense LLMs requires enormous amounts of data and centralized compute, which introduces fundamental bottlenecks and ever-growing costs for large models. Several studies aim…
Privacy-Preserving Aggregation for Decentralized Learning with Byzantine-Robustness
Ali Reza Ghavamipour, Benjamin Zi Hao Zhao, Oguzhan Ersoy +1
Decentralized machine learning (DL) has been receiving an increasing interest recently due to the elimination of a single point of failure, present in Federated learning setting. Y…