Weaver_zhu

4 mesi fadiscuss

Agree, but I guess the Opus 4.6 is 10x larger, rather than Chinese models being 10x more efficient. It is said that GPT-4 is already a 1.6T model, and Llama 4 behemoth is also much bigger than Chinese open-weight models. Chinese tech companies are short of frontier GPUs, but they did a lot of innovations on inference efficiency (Deepseek CEO Liang himself shows up in the author list of the related published papers).

Weaver_zhu

9 mesi fadiscuss

I recall recent work [ACE](https://www.arxiv.org/abs/2510.04618) and [GEPA](https://arxiv.org/abs/2507.19457) where models get improved by adapting and adopting different kinds of prompt. The improvements will be expected to be more generalized than fine-tuning.

Weaver_zhu

9 mesi fadiscuss

IMO the author is a little over-claiming this work by naming 'recursive'. Quote from this blog:

> Lastly, in our experiments we only consider a recursive depth of 1 — i.e. the root LM can only call LMs, not other RLMs.

> but we felt that for most modern “long context” benchmarks, a recursive depth of 1 was sufficient to handle most problems.

I don't think a size 2 call stack algorithm should be regarded as 'recursive'.

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