Scaling Laws in LLMs
Discussions focus on the effectiveness of scaling compute and data for LLMs, with some evidence of diminishing returns and efficiency gains. This sub-topic explores how scaling drives progress but raises concerns about sustainability.
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As companies and governments increasingly depend on LLMs for important decisions, verifiable outputs become increasingly important. Great demo!
A truely generative meta-model of activations, for steering, probing, and understanding LLMs at scale!
Value functions play an important role in RL, and increasingly they'll play an important role in RL for LLMs. This new paper led by @rohin_manvi is one step in this direction: using value functions to optimize test-time compute with adaptive computation.
Debug your model with StringSight: LLMs all the way down!
Super excited about our new work on pretrained 4-D robotic foundation models. LLMs learned with 4-D representations on egocentric datasets transfer well to real world tasks!