James Thornton

@jamesthornton.bsky.social

Research scientist at Google DeepMind. Creating noise from data.

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Check out our Apple research work on scaling laws for native multimodal models! Combined with mixtures of experts, native models develop both specialized and multimodal representations! Lots of rich findings and opportunists for follow up research!

arXiv cs.CV Computer Vision and Pattern Recognition@cscv-bot.bsky.social · last yr.

Shukor, Fini, da Costa, Cord, Susskind, El-Nouby: Scaling Laws for Native Multimodal Models Scaling Laws for Native Multimodal Models https://arxiv.org/abs/2504.07951 https://arxiv.org/pdf/2504.07951 https://arxiv.org/html/2504.07951

Hiring two student researchers for Gemma post-training team at @GoogleDeepMind Paris! First topic is about diversity in RL for LLMs (merging, generalization, exploration & creativity), second is about distillation. Ideal if you're finishing PhD. DMs open!

I'm increasingly uncomfortable with the argument (read more and more often) that rectified flow (without reflow steps) offers straighter trajectories than diffusion (in the Gaussian case), despite being a diffusion model itself with special noise schedule... It seems it comes from the confusion 1/2

🚨 One question that has always intrigued me is the role of different ways to increase a model's capacity: parameters, parallelizable compute, or sequential compute? We explored this through the lens of MoEs:

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The thing about "AI progress is hitting a wall" is that AI progress (like most scientific research) is a maze, and the way you solve a maze is by constantly hitting walls and changing directions.