On the Limitations of Compute Thresholds as a Governance Strategy · HackerPapers discussions

On the Limitations of Compute Thresholds as a Governance Strategy — mentioned by godelski

Thu, 04 Sep 2025 03:43:03 GMT

> this isn't really about whether scaling is "dead" I think there's a good position paper by Sara Hooker[0] that mentions some of this. Key point being that while the frontier is being pushed by big models with big data there's a very quiet revolution of models using far fewer parameters (still quite big) and data. Maybe "Scale Is All You Need"[1], but that doesn't mean it is practical or even a good approach. It's a shame these research paths have gotten a lot of pushback, especially given today's concerns about inference costs (this pushback still doesn't seem to be decreasing) > verifiable…

Paper and discussion history. A mention is not an endorsement.

On the Limitations of Compute Thresholds as a Governance Strategy — mentioned by godelski

Tue, 20 May 2025 21:37:56 GMT

Not necessarily. The prevailing paradigm is that performance scales with size (of data and compute power). Of course, this is observably false as we have a long list of smaller models that require fewer resources to train and/or deploy with equal or better performance than larger ones. That's without using distillation, reduced precision/quantization, pruning, or similar techniques[0]. The real thing we need is more investment into reducing computational resources to train and deploy models and to do model optimization (best example being Llama CPP). I can tell you from personal experience…

Paper and discussion history. A mention is not an endorsement.

On the Limitations of Compute Thresholds as a Governance Strategy — HN submission

Mon, 22 Jul 2024 14:57:21 GMT

The Limitations of Compute Thresholds as a Governance Strategy

1 point; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.