Cohere's Rachad Alao on open science for foundation models
The talk links open model releases to concrete security practices like independent auditing and dynamic safeguards, rather than treating openness and safety as opposing goals. Researchers and ML engineers can follow the named stack — PyTorch, TorchTitan and vLLM — to reproduce or audit open models themselves. Cohere's stance is notable as a US-listed enterprise lab that publicly backs open science. origin
Rachad Alao of Cohere spoke at PyTorchCon 2026 on how open science can support transparent and secure AI foundation model development. He argued for publishing model architectures, training strategies, parameter choices and evaluation methodologies. He paired this with an evidence-based risk framework, independent auditing, continuous monitoring and dynamic safeguards. He also covered how PyTorch, TorchTitan and vLLM support model development and inference optimisation when models are open, and how access to internal representations enables research in retrieval, reasoning and multilingual AI.
Rachad Alao (@cohere) examines how open science can support transparent and secure AI foundation model development at #PyTorchCon 2026.
Rachad argues for publishing model architectures, training strategies, parameter choices, and evaluation methodologies, alongside an evidence-based risk framework, independent auditing, continuous monitoring, and dynamic safeguards.
He discusses how PyTorch, TorchTitan, and @vllm_project support model development and inference optimization when models are open and accessible, and how access to internal representations enables research in retrieval, reasoning, and multilingual AI.
