4 papers · 1 filter
Where Should RL Post-Training Compute Go? Model Size, Search, Learning, and Feedback
Patrick Wilhelm, Odej Kao
Reinforcement Learning (RL) post-training is increasingly used to adapt foundation models for reasoning, planning, and feedback-driven robot-learning pipelines, but constrained pos…
Exploring Silent Data Corruption as a Reliability Challenge in LLM Training
Anton Altenbernd, Philipp Wiesner, Odej Kao
As Large Language Models (LLMs) scale in size and complexity, the consequences of failures during training become increasingly severe. A major challenge arises from Silent Data Cor…
Revisiting Gradient Staleness: Evaluating Distance Metrics for Asynchronous Federated Learning Aggregation
Patrick Wilhelm, Odej Kao
In asynchronous federated learning (FL), client devices send updates to a central server at varying times based on their computational speed, often using stale versions of the glob…
Noise-aware Client Selection for carbon-efficient Federated Learning via Gradient Norm Thresholding
Patrick Wilhelm, Inese Yilmaz, Odej Kao
Training large-scale Neural Networks requires substantial computational power and energy. Federated Learning enables distributed model training across geospatially distributed data…