ImportAI 449: LLMs training other LLMs; 72B distributed training run; computer vision is harder than generative text

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What Happened

Welcome to Import AI, a newsletter about AI research. Import AI runs on arXiv and feedback from readers. If you’d like to support this, please subscribe. Subscribe now Can LLMs autonomously refine other LLMs for new tasks? Somewhat.…PostTrainBench shows startling growth in AI capabilities at post-tr

Our Take

Can LLMs autonomously refine other LLMs? Only if you give them the right objective function and the necessary computational freedom. The distributed training run on 72B parameters isn't just a benchmark; it highlights the insane computational overhead required just to squeeze incremental gains out of massive distributed systems. It’s a pure exercise in resource management masquerading as intelligence.

PostTrainBench shows growth because the sheer brute force of distributed training forces emergent capabilities, but that growth is often brittle. We're not just training smarter models; we're building systems that require massive, opaque compute stacks just to stay functional. The computer vision aspect proves that complex perception tasks are exponentially harder than pure text generation.

Honestly, the bottleneck isn't the model architecture; it's the physics of distributing that kind of complex cognitive load across thousands of GPUs without collapsing the entire cluster. We're spending obscene amounts on compute for marginal gains in generalized reasoning.

What To Do

Focus R&D on novel memory architectures and sparse activation techniques to reduce the compute footprint of multi-LLM training runs. Impact:medium

Builder's Brief

Who

ML researchers and teams running large-scale training experiments

What changes

distributed training at 72B scale is more accessible; self-refinement loops may reduce annotation costs for fine-tuning

When

weeks

Watch for

replication of self-refinement gains on a held-out benchmark not used during the refinement loop

What Skeptics Say

LLMs autonomously refining other LLMs compounds alignment drift and benchmark overfitting; eval gains in self-improvement loops have yet to demonstrate transfer to out-of-distribution real tasks, and the 72B distributed training result is a methods paper, not a capability breakthrough. Computer vision being 'harder' is a restatement of a known problem, not a solution.

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Import AI ImportAI 449: LLMs training other LLMs; 72B distributed training run; computer vision is harder than generative text