collaborators

7 papers

cs.LG2026

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…

cs.CR2026

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…

cs.LG2025

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…

cs.LG2025

Go With The Flow: Churn-Tolerant Decentralized Training of Large Language Models

Nikolay Blagoev, Bart Cox, Jérémie Decouchant +1

Motivated by the emergence of large language models (LLMs) and the importance of democratizing their training, we propose GWTF, the first crash tolerant practical decentralized tra…

cs.DC2025

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…

cs.LG2025

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…

cs.LG2025

SkipPipe: Partial and Reordered Pipelining Framework for Training LLMs in Heterogeneous Networks

Nikolay Blagoev, Lydia Yiyu Chen, Oğuzhan Ersoy

Data and pipeline parallelism are ubiquitous for training of Large Language Models (LLM) on distributed nodes. Driven by the need for cost-effective training, recent work explores…