Aleksei Romanov

Ex-Apple GenAI engineer, building an LLM post-training platform at g factor technologies

Joined on Sep 21, 2026 https://www.g-ftech.com

Aleksei Romanov for g factor

Sep 28

From RLHF to RLVR: The Evolution of Reward Signals and the Battle Against Reward Hacking

#machinelearning #reinforcementlearning #llm #security

9 min read

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Aleksei Romanov for g factor

Sep 27

AsyncGRPO: Eliminating GPU Idle Bubbles in Environment-Heavy RL Post-Training

#machinelearning #reinforcementlearning #llm #gpu

9 min read

Aleksei Romanov for g factor

Sep 27

Owning vs. Renting Intelligence: Why Enterprises Are Building Sovereign AI

#ai #llm #machinelearning #mlops

9 min read

Aleksei Romanov for g factor

Sep 26

LoRA & DoRA: The Math, Memory, and Trade-offs

#machinelearning #ai #llm #deeplearning

8 min read

Aleksei Romanov for g factor

Sep 25

Multi-Reward Reinforcement Learning for LLM Agents: Comparing PPO, GRPO, DAPO, and GDPO

#machinelearning #ai #llm #reinforcementlearning

8 min read

Aleksei Romanov for g factor

Sep 24

SFT vs. RL: What Changes Inside the Model?

#machinelearning #ai #llm #deeplearning

6 min read

Aleksei Romanov for g factor

Sep 23

Latent-GRPO: Reinforcement Learning in Continuous Thought Space

#grpo #machinelearning #ai #llm

9 min read

Aleksei Romanov for g factor

Sep 21

High-Throughput LLM Inference & Training: A Deep Dive into vLLM

#ai #machinelearning #python #gpu

9 min read

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Aleksei Romanov

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g factor

From RLHF to RLVR: The Evolution of Reward Signals and the Battle Against Reward Hacking

Want to connect with Aleksei Romanov?

AsyncGRPO: Eliminating GPU Idle Bubbles in Environment-Heavy RL Post-Training

Owning vs. Renting Intelligence: Why Enterprises Are Building Sovereign AI

LoRA & DoRA: The Math, Memory, and Trade-offs

Multi-Reward Reinforcement Learning for LLM Agents: Comparing PPO, GRPO, DAPO, and GDPO

SFT vs. RL: What Changes Inside the Model?

Latent-GRPO: Reinforcement Learning in Continuous Thought Space

High-Throughput LLM Inference & Training: A Deep Dive into vLLM